CNS AP-axis VAE model

This notebook runs through the CNS analysis used in the VAE paper (ref: ).

We use genes that were found to be differentially expressed and aim to understand how different groups of genes responded to PRC2 knockout.

We do this by using a variational autoencoder (VAE).

Our VAE model uses a three layer network, and as input data, takes in the normalised expression matrix including the wild type and response to Eed-cKO.

Sections:

Part 1: VAE setup

1) Function setup
2) Read in processed dataframe and organise this
3) Build VAE model

Part 2: VAE as a ranking method

4) Setup data for visualisations
5) Quantify separability of latent space
6) Save VAE ranks to csv to run GSEA in R

Part 3: VAE to identify coordinating groups of genes

6) Build gene groups using VAE latent space
7) Save gene groups for ORA in R
8) Run motif anlaysis using FIMO on gene groups
9) Inspecting the groups in context of chromHMM annotations 
10) Inspect groups in terms of annotations for Epi paper
In [1]:
"""
--------------------------------------------------------
                     Imports
--------------------------------------------------------
"""

import os, sys
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import venn
from matplotlib_venn import venn3
from sklearn.decomposition import PCA
from scipy import stats
from sciviso import *
from sciutil import SciUtil
from scivae import Vis as VVis
import string
u = SciUtil()

module_path = os.path.abspath(os.path.join(''))
sys.path.append(module_path)


"""
--------------------------------------------------------
                     Global variables
--------------------------------------------------------
"""
date = '20210217'

gene_id = 'entrezgene_id'
gene_name = 'external_gene_name'

sci_colour = ['#483873', '#1BD8A6', '#B117B7', '#AAC7E2', '#FFC107', '#016957', '#9785C0', 
             '#D09139', '#338A03', '#FF69A1', '#5930B1', '#FFE884', '#35B567', '#1E88E5', 
             '#ACAD60', '#A2FFB4', '#B618F5', '#854A9C']

hist_colour = '#483873'

e11_colour = '#BEC0C2'
e13_colour = '#A7A9AC'
e15_colour = '#7D7E81'
e18_colour = '#58595B'

fb_colour = '#ffbf80'
mb_colour = '#ff8c1a'
hb_colour = '#b35900'
sc_colour = '#663300'
fb_color = '#ffbf80'
mb_color = '#ff8c1a'
hb_color = '#b35900'
sc_color = '#663300'

h3k36me3_colour = '#CEF471'
h3k27me3_colour = '#9F71F4'
h3k4me3_colour = '#9F00FA'
h3k4me2_colour = '#5930B1'
h3k4me1_colour = '#FFE884'
h3k27ac_colour = '#35B567'
h3k9me3_colour = '#1E88E5'
h3k9ac_colour = '#A2FFB4'
           
wt_colour = '#AADFF1'
ko_colour = '#A53736'

sci_colour = ['#483873', '#1BD8A6', '#B117B7', '#AAC7E2', '#FFC107', '#016957', '#9785C0', 
             '#D09139', '#338A03', '#FF69A1', '#5930B1', '#FFE884', '#35B567', '#1E88E5', 
             '#ACAD60', '#A2FFB4', '#B618F5', '#854A9C']
sns.palplot(sci_colour)
sns.color_palette(sci_colour)

project_name = '3_node_consistent_genes'

data_dir = '../../data/'
r_dir = f'{data_dir}results/deseq2/'
fig_dir = f'../../figures/{project_name}/vae/'
output_dir = f'{data_dir}results/{project_name}/vae/'
input_dir = f'{data_dir}results/prelim/'
rna_dir = f'{input_dir}feature-counts_04052020/'
supp_dir = f'{data_dir}input/supps/'
logging_dir = 'logging/'
ora_dir = '../../data/results/3_node_consistent_genes/functional/'

grey = '#bdbdbd'
hist_cmap = 'Greens'
rna_cmap = 'Purples'
div_cmap = 'seismic'

experiment_name = project_name
from_saved = True  # Load from a saved VAE
In [2]:
df_all = pd.read_csv(f'{input_dir}df-all_epi-2500_20210124.csv')
df_training = pd.read_csv(f'{input_dir}df-consistent_epi-2500_20210124.csv')
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3062: DtypeWarning: Columns (4) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

1) Function setup

Here we just initialise the functions that will be used in the notebook.

In [3]:
"""
--------------------------------------------------------
                     Colour/style getters
--------------------------------------------------------
"""
def get_time_colour(c):
    if '11' in c or '10' in c:
        return e11_colour
    elif '13' in c or '12' in c:
        return e13_colour
    elif '15' in c or '14' in c:
        return e15_colour
    elif '18' in c or '16' in c:
        return e18_colour
    return '#FFFFFF'

def get_tissue_colour(c):
    if 'sc' in c or 'spinal' in c:
        return sc_colour
    elif 'hb' in c or 'hindbrain' in c:
        return hb_colour
    elif 'fb' in c or 'forebrain' in c:
        return fb_colour
    elif 'mb' in c or 'midbrain' in c:
        return mb_colour
    return '#FFFFFF'

def get_cond_colour(c):
    if 'ko' in c:
        return ko_colour
    elif 'wt' in c:
        return wt_colour
    return '#FFFFFF'

def get_mark_colour(c):
    if '36me3' in c:
        return h3k36me3_colour
    elif '27me3' in c:
        return h3k27me3_colour
    elif 'K4me3' in c:
        return h3k4me3_colour
    elif 'K4me2' in c:
        return h3k4me2_colour
    elif 'K4me1' in c:
        return h3k4me1_colour
    elif '27ac' in c:
        return h3k27ac_colour
    elif 'K9me3' in c:
        return h3k9me3_colour
    elif 'K9ac' in c:
        return h3k9ac_colour
    return '#FFFFFF'

"""
--------------------------------------------------------
                     Plotting
--------------------------------------------------------
"""
def pplot():
    return
        
def cplot():
    return
    
def save_fig(title):
    plt.savefig(f'{fig_dir}{title.replace(" ", "-")}_{experiment_name}_{date}.svg')
    
"""
--------------------------------------------------------
                     Heatmap and vis function
--------------------------------------------------------
"""

def plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes, 
                      input_cols, vae_data, vae, gene_name, mark, method='input', 
                      title="", merge_reps=False, input_col_ids=None, cmap='', genes=None, 
                      values=None):
    brain_genes = []
    colours = []
    gene_idxs = []
    genes = genes if genes is not None else df[gene_name].values
    i = 0
    for g in genes:
        if g in fb_genes:
            brain_genes.append(g)
            colours.append(fb_colour)
            gene_idxs.append(i)
        i += 1
    i = 0
    for g in genes:    
        if g in mb_genes:
            brain_genes.append(g)
            colours.append(mb_colour)
            gene_idxs.append(i)
        i += 1
    i = 0
    for g in genes:    
        if g in hb_genes:
            brain_genes.append(g)
            colours.append(hb_colour)
            gene_idxs.append(i)
        i += 1
    i = 0
    for g in genes:    
        if g in sc_genes:
            brain_genes.append(g)
            colours.append(sc_colour)
            gene_idxs.append(i)
        i += 1
    heatmap_df = pd.DataFrame()
    heatmap_df[gene_name] = brain_genes

    if method == 'decoding':
        deocded = vae.decoder.predict(vae_data)
        deocded = scaler.fit_transform(deocded)
        vals = deocded[gene_idxs]
    elif method == 'input':
        vals = df[input_cols].values[gene_idxs]
    else:
        vals = vae_data[gene_idxs]
    vals = values[gene_idxs] if values is not None else vals
    
    vals = np.nan_to_num(vals)
    min_r = np.min(vals, axis = 1)
    max_r = np.max(vals, axis = 1)
    ov_c = max_r - min_r
    if not merge_reps:
        cols = []
        for i, c in enumerate(input_cols):
            if input_col_ids is not None:
                if i in input_col_ids:
                    heatmap_df[c] = np.nan_to_num(vals[:,i])
                    cols.append(c)
            else:
                heatmap_df[c] = np.nan_to_num(vals[:,i])
                cols.append(c)
        heatmap_cols = cols
    else:
        i = 0
        offset = 0
        max_c = len(input_cols)
        if input_col_ids is not None:
            offset = input_col_ids[0]
            max_c = len(input_col_ids)
        heatmap_cols = []
        
        while(i < max_c):
            if input_col_ids is not None:
                heatmap_df[input_cols[input_col_ids[i]]] = (vals[:, input_col_ids[i]] - min_r)/(max_r - min_r)
                heatmap_cols.append(input_cols[input_col_ids[i]])
            else:
                heatmap_df[input_cols[i]] = (vals[:, i + offset] - min_r)/(max_r - min_r)
                heatmap_cols.append(input_cols[i])
            i += 1
            
    print(heatmap_df.head())
    cols = []
    col_colours = []
    cond_colours = []
    tissue_colours = []
    time_colours = []
    for c in heatmap_df.columns:
        if 'wt' in c or 'ko' in c:
            cols.append(c)
            time_colours.append(get_time_colour(c))
            tissue_colours.append(get_tissue_colour(c))
            cond_colours.append(get_cond_colour(c))
        if mark in c and hist_metric in c:
            time_colours.append(get_time_colour(c))
            tissue_colours.append(get_tissue_colour(c))
            cond_colours.append('white')
            cols.append(c)
        elif 'log2' in c:
            time_colours.append(get_time_colour(c))
            tissue_colours.append(get_tissue_colour(c))
            cond_colours.append(get_mark_colour(c))
            cols.append(c)
    col_colours = [cond_colours, tissue_colours, time_colours]

    # Now plot the rnaseq response as a heatmap
    vmin = None
    vmax = None
    if cmap == hist_cmap or cmap == rna_cmap:
        vmin = 0
        vmax = 10
    elif cmap == div_cmap:
        vmin = -3
        vmax = 3
    heatmap = Heatmap(heatmap_df, heatmap_cols, gene_name,
                      title=title, cluster_cols=False, cluster_rows=False, vmin=vmin, vmax=vmax,
                      figsize=(5, 5), col_colours=col_colours,
                      cmap=cmap)
    heatmap.cmap = cmap
    heatmap.plot()
    pplot()
    save_fig(title)
    plt.show()

def plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes, 
                      input_cols, vae_data, vae, gene_name, mark, method='input', 
                      title="", merge_reps=False, input_col_ids=None, cmap='', genes=None, 
                      values=None):
    brain_genes = []
    colours = []
    gene_idxs = []
    genes = genes if genes is not None else df[gene_name].values
    i = 0
    for g in genes:
        if g in fb_genes:
            brain_genes.append(g)
            colours.append(fb_colour)
            gene_idxs.append(i)
        i += 1
    i = 0
    for g in genes:    
        if g in mb_genes:
            brain_genes.append(g)
            colours.append(mb_colour)
            gene_idxs.append(i)
        i += 1
    i = 0
    for g in genes:    
        if g in hb_genes:
            brain_genes.append(g)
            colours.append(hb_colour)
            gene_idxs.append(i)
        i += 1
    i = 0
    for g in genes:    
        if g in sc_genes:
            brain_genes.append(g)
            colours.append(sc_colour)
            gene_idxs.append(i)
        i += 1
    heatmap_df = pd.DataFrame()
    heatmap_df[gene_name] = brain_genes

    if method == 'decoding':
        deocded = vae.decoder.predict(vae_data)
        deocded = scaler.fit_transform(deocded)
        vals = deocded[gene_idxs]
    elif method == 'input':
        vals = df[input_cols].values[gene_idxs]
    else:
        vals = vae_data[gene_idxs]
    vals = values[gene_idxs] if values is not None else vals
    
    vals = np.nan_to_num(vals)
    min_r = np.min(vals, axis = 1)
    max_r = np.max(vals, axis = 1)
    ov_c = max_r - min_r
    if not merge_reps:
        cols = []
        for i, c in enumerate(input_cols):
            if input_col_ids is not None:
                if i in input_col_ids:
                    heatmap_df[c] = np.nan_to_num(vals[:,i])
                    cols.append(c)
            else:
                heatmap_df[c] = np.nan_to_num(vals[:,i])
                cols.append(c)
        heatmap_cols = cols
    else:
        i = 0
        offset = 0
        max_c = len(input_cols)
        if input_col_ids is not None:
            offset = input_col_ids[0]
            max_c = len(input_col_ids)
        heatmap_cols = []
        
        while(i < max_c):
            if input_col_ids is not None:
                heatmap_df[input_cols[input_col_ids[i]]] = (vals[:, input_col_ids[i]] - min_r)/(max_r - min_r)
                heatmap_cols.append(input_cols[input_col_ids[i]])
            else:
                heatmap_df[input_cols[i]] = (vals[:, i + offset] - min_r)/(max_r - min_r)
                heatmap_cols.append(input_cols[i])
            i += 1
            
    print(heatmap_df.head())
    cols = []
    col_colours = []
    cond_colours = []
    tissue_colours = []
    time_colours = []
    for c in heatmap_df.columns:
        if 'wt' in c or 'ko' in c:
            cols.append(c)
            time_colours.append(get_time_colour(c))
            tissue_colours.append(get_tissue_colour(c))
            cond_colours.append(get_cond_colour(c))
        if mark in c and hist_metric in c:
            time_colours.append(get_time_colour(c))
            tissue_colours.append(get_tissue_colour(c))
            cond_colours.append('white')
            cols.append(c)
        elif 'log2' in c:
            time_colours.append(get_time_colour(c))
            tissue_colours.append(get_tissue_colour(c))
            cond_colours.append(get_mark_colour(c))
            cols.append(c)
    col_colours = [cond_colours, tissue_colours, time_colours]

    # Now plot the rnaseq response as a heatmap
    vmin = None
    vmax = None
    if cmap == hist_cmap or cmap == rna_cmap:
        vmin = 0
        vmax = 10
    elif cmap == div_cmap:
        vmin = -3
        vmax = 3
    heatmap = Heatmap(heatmap_df, heatmap_cols, gene_name,
                      title=title, cluster_cols=False, cluster_rows=False, vmin=vmin, vmax=vmax,
                      figsize=(5, 5),
                      cmap=cmap)
    heatmap.cmap = cmap
    heatmap.plot()
    pplot()
    save_fig(title)
    plt.show()

2) Read in processed dataframe

Here we import the results from running the AP-axis dataset generation script.

This used the output from differential analysis and also annotated histone marks to genes.

In [4]:
# Remove genes that weren't able to map to a gene name
gene_mask = []
for g in df_training[gene_name].values:
    if g == None:
        gene_mask.append(0)
    else:
        try:
            e = float(g)
            gene_mask.append(1)
        except ValueError:
            gene_mask.append(0)

df_training['gene_mask'] = gene_mask
print(len(df_training))
print(len(df_training[df_training['gene_mask'] == 0]))
df_training = df_training[df_training['gene_mask'] == 0]

# Smooth out the columns in the data frame i.e. for the clones we only put in the mean of the two replicates

cols_to_merge = [c for c in df_training.columns if 'wt' in c or 'ko' in c]
col_names = []
col_values = []
values = df_training[cols_to_merge].values
i = 0
while(i < len(cols_to_merge)):
    df_training[f'{cols_to_merge[i][:-1]}_merged-rep'] = 0.5 * (df_training[cols_to_merge[i]].values +
                                                                df_training[cols_to_merge[i + 1]].values)
    print("merged", cols_to_merge[i], cols_to_merge[i + 1])
    i += 2
1371
1371
merged wt11fb1 wt11fb2
merged wt13fb1 wt13fb2
merged wt15fb1 wt15fb2
merged wt18fb1 wt18fb2
merged wt11mb1 wt11mb2
merged wt13mb1 wt13mb2
merged wt15mb1 wt15mb2
merged wt18mb1 wt18mb2
merged wt11hb1 wt11hb2
merged wt13hb1 wt13hb2
merged wt15hb1 wt15hb2
merged wt18hb1 wt18hb2
merged wt11sc1 wt11sc2
merged wt13sc1 wt13sc2
merged wt15sc1 wt15sc2
merged wt18sc1 wt18sc2
merged ko11fb1 ko11fb2
merged ko13fb1 ko13fb2
merged ko15fb1 ko15fb2
merged ko18fb1 ko18fb2
merged ko11mb1 ko11mb2
merged ko13mb1 ko13mb2
merged ko15mb1 ko15mb2
merged ko18mb1 ko18mb2
merged ko11hb1 ko11hb2
merged ko13hb1 ko13hb2
merged ko15hb1 ko15hb2
merged ko18hb1 ko18hb2
merged ko11sc1 ko11sc2
merged ko13sc1 ko13sc2
merged ko15sc1 ko15sc2
merged ko18sc1 ko18sc2
In [5]:
cols_to_merge = [c for c in df_all.columns if 'wt' in c or 'ko' in c]
col_names = []
col_values = []
values = df_all[cols_to_merge].values
i = 0
while(i < len(cols_to_merge)):
    df_all[f'{cols_to_merge[i][:-1]}_merged-rep'] = 0.5 * (df_all[cols_to_merge[i]].values +
                                                                df_all[cols_to_merge[i + 1]].values)
    i += 2

Look at Pro and anti-prolif genes in the const aff DF

In [6]:
## Print out the pro and antiproliferation genes
pro_prolif = ['Ccna1', 'Ccna2', 'Ccnb1', 'Ccnb2', 'Ccnb3', 'Cdc25a', 'Cdc25b', 'Cdc25c', 'Cdk1',  'Wee2', 
        'Ccnd1', 'E2f1', 'E2f2', 'E2f3', 'Cdk2', 'Cdk4', 'Cdk6', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2']
anti_prolif = ['Chek1', 'Chek2', 'Wee1', 'Cdkn1a', 'Cdkn1b', 'Cdkn2a', 'Cdkn1c', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d',  'Rb1']

u.dp(["Const aff pro-prolif:"])
for g in df_training[gene_name].values:
    if g in pro_prolif:
        print(g)

u.dp(["Const aff anti-prolif:"])
for g in df_training[gene_name].values:
    if g in anti_prolif:
        print(g)
--------------------------------------------------------------------------------
                             Const aff pro-prolif:	                             
--------------------------------------------------------------------------------
Ccnb1
Cdc25c
E2f1
Ccna2
Ccnd1
--------------------------------------------------------------------------------
                            Const aff anti-prolif:	                             
--------------------------------------------------------------------------------
Cdkn1a
Cdkn2a
Cdkn2b

3) Build VAE model

Since the VAEs are stochastic rather than deterministic we want to create our clusters based on a number of iterations. We also are interested in how much the genes differ in various latent spaces.

In [7]:
from scivae import VAE

hist_metric = 'signal'
labels = df_training[gene_name].values


# -----------------------------------------------------------------------------------
#                         Set the input data space
# -----------------------------------------------------------------------------------
def get_input_data(df: pd.DataFrame, label_col: str):
    scaler = MinMaxScaler(copy=True)
    """ Here we define the data for the VAE we choose to log2 the signal """
    cols_bin = []
    tmp_df = pd.DataFrame()
    non_nan_df = df.copy()
    non_nan_df = non_nan_df.fillna(0)
    for c in df.columns:
        if 'H3K27me3' in c and 'signal' in c and 'brain' in c:
            v = np.log2(non_nan_df[c].values + 1)
            nn_min = np.nanmin(v)
            nn_max = np.nanmax(v)
            tmp_df[f'{c}_log2'] = (v-nn_min) / (nn_max - nn_min)
            cols_bin.append(f'{c}_log2')
        elif ('merged-rep' not in c) and ('log2FoldChange' in c or 'wt' in c or 'ko' in c): 
            cols_bin.append(c)
            v = non_nan_df[c].values
            nn_min = np.nanmin(v)
            nn_max = np.nanmax(v)
            tmp_df[c] = (v-nn_min) / (nn_max - nn_min)
    vae_values = tmp_df[cols_bin].values
    vae_values = np.nan_to_num(vae_values)
    tmp_df[label_col] = df[label_col].values
    return vae_values, tmp_df, df[label_col].values
In [9]:
# # -----------------------------------------------------------------------------------
# #                         Run a test to work out the best number of nodes
# # -----------------------------------------------------------------------------------
# vae_input_values, df_input, labels = get_input_data(df_training, gene_name)
# tsts = [1, 2, 3, 4, 6, 8, 16, 24]
# for i in tsts:
#     for j in range(0, 5):
#         config = {'loss': {'loss_type': 'mse', 'distance_metric': 'mmd', 'mmd_weight': 2.0}, 
#                   'encoding': {'layers': [{'num_nodes': 64, 'activation_fn': 'selu'}, 
#                                           {'num_nodes': 32, 'activation_fn': 'relu'}]}, 
#                   'decoding': {'layers': [{'num_nodes': 32, 'activation_fn': 'relu'},
#                                           {'num_nodes': 64, 'activation_fn': 'selu'}]}, 
#                   'latent': {'num_nodes': i}, 'optimiser': {'params': {}, 'name': 'adam'}}

#         vae_mse = VAE(vae_input_values, vae_input_values, labels, config, f'vae_num-nodes_{i}_rep_{j}')
#         vae_mse.encode('default', epochs=100, batch_size=50,
#                        logging_dir=logging_dir, logfile=f'run_num-nodes_{i}_rep_{j}.csv')

# # -----------------------------------------------------------------------------------
# #                         Plot results
# # -----------------------------------------------------------------------------------

# # Read in each of the csv's and then add a line for each one
# tsts = [1, 2, 3, 4, 6, 8, 16, 24]
# fig, ax = plt.subplots()
# labels = []
# for i, t in enumerate(tsts):
#     c = sci_colour[i]
#     print(c, i)
#     for j in range(0, 5):
#         d = pd.read_csv(f'{logging_dir}run_num-nodes_{t}_rep_{j}.csv')
#         print(f'{logging_dir}run_num-nodes_{t}_rep_{j}.csv')
#         ax.plot(d['epoch'].values, d['loss'].values, c=c, alpha=0.5)
#         label=f'{t} node(s)'
#         if label not in labels:
#             ax.plot(d['epoch'].values, d['val_loss'].values, c=c, alpha=0.8, label=label)
#             labels.append(label)
#         else:
#             ax.plot(d['epoch'].values, d['val_loss'].values, c=c, alpha=0.8)
        
# ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
# axes = plt.gca()
# axes.set_ylim([0, 5])
# save_fig(f'node_num')
# plt.show()
In [210]:
# -----------------------------------------------------------------------------------
#                         Based on the above, set the number of nodes
#                         1) Build non-linear VAE
#                         2) Build linear VAE
# -----------------------------------------------------------------------------------
num_nodes = 3
vae_input_values, df_input, labels = get_input_data(df_training, gene_name)

config = {'loss': {'loss_type': 'mse', 'distance_metric': 'mmd', 'mmd_weight': 1.0, 
                   'mmcd_method': 'k'}, 
                  'encoding': {'layers': [{'num_nodes': 64, 'activation_fn': 'selu'}, 
                                          {'num_nodes': 32, 'activation_fn': 'relu'}
                                          ]}, 
                  'decoding': {'layers': [
                                          {'num_nodes': 32, 'activation_fn': 'relu'},
                                          {'num_nodes': 64, 'activation_fn': 'selu'}]}, 
                  'latent': {'num_nodes': num_nodes}, 'optimiser': {'params': {}, 'name': 'adam'}}

vae_mse = VAE(vae_input_values, vae_input_values, labels, config, f'final')
if not from_saved:
    vae_mse.encode('default', epochs=250, batch_size=50,
                   logging_dir="final_vae_logs", logfile=f'final.csv')
    vae_mse.save() # save the VAE
    vae_mse.u.dp(["Saved VAE to current directory."])
else:
    vae_mse.u.dp(["Trying to load saved VAE..."])
    vae_mse.load()
    vae_mse.u.dp(["Loaded saved VAE :) "])

df = df_training.copy()

# Encode the values using the VAE
scaler = MinMaxScaler(copy=True)
scaled_vals = scaler.fit_transform(vae_input_values)
vae_data = vae_mse.encode_new_data(scaled_vals)
None
--------------------------------------------------------------------------------
                          Trying to load saved VAE...	                          
--------------------------------------------------------------------------------
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_186 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_187 (Dense)               (None, 32)           2080        dense_186[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_187[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_187[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_188 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_189 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_190 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "final_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_186 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_187 (Dense)               (None, 32)           2080        dense_186[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_187[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_187[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_620 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_620 ( [()]                 0           tf_op_layer_Shape_620[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_62 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_620[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_62[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_186 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_62 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_186[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_621 (TensorFl [(2,)]               0           tf_op_layer_Add_62[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_623 (TensorFl [(2,)]               0           tf_op_layer_Add_62[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_622 (TensorFl [(2,)]               0           tf_op_layer_Add_62[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_624 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_626 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_625 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_627 (TensorFl [(2,)]               0           tf_op_layer_Add_62[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_629 (TensorFl [(2,)]               0           tf_op_layer_Add_62[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_628 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_621 ( [()]                 0           tf_op_layer_Shape_621[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_623 ( [()]                 0           tf_op_layer_Shape_623[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_622 ( [()]                 0           tf_op_layer_Shape_622[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_624 ( [()]                 0           tf_op_layer_Shape_624[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_626 ( [()]                 0           tf_op_layer_Shape_626[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_625 ( [()]                 0           tf_op_layer_Shape_625[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_627 ( [()]                 0           tf_op_layer_Shape_627[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_629 ( [()]                 0           tf_op_layer_Shape_629[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_628 ( [()]                 0           tf_op_layer_Shape_628[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_372/shape ( [(3,)]               0           tf_op_layer_strided_slice_621[0][
                                                                 tf_op_layer_strided_slice_623[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_373/shape ( [(3,)]               0           tf_op_layer_strided_slice_622[0][
                                                                 tf_op_layer_strided_slice_623[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_374/shape ( [(3,)]               0           tf_op_layer_strided_slice_624[0][
                                                                 tf_op_layer_strided_slice_626[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_375/shape ( [(3,)]               0           tf_op_layer_strided_slice_625[0][
                                                                 tf_op_layer_strided_slice_626[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_376/shape ( [(3,)]               0           tf_op_layer_strided_slice_627[0][
                                                                 tf_op_layer_strided_slice_629[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_377/shape ( [(3,)]               0           tf_op_layer_strided_slice_628[0][
                                                                 tf_op_layer_strided_slice_629[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_372 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_62[0][0]         
                                                                 tf_op_layer_Reshape_372/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_372/multiples  [(3,)]               0           tf_op_layer_strided_slice_622[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_373 (Tensor [(1, None, None)]    0           tf_op_layer_Add_62[0][0]         
                                                                 tf_op_layer_Reshape_373/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_373/multiples  [(3,)]               0           tf_op_layer_strided_slice_621[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_374 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_374/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_374/multiples  [(3,)]               0           tf_op_layer_strided_slice_625[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_375 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_375/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_375/multiples  [(3,)]               0           tf_op_layer_strided_slice_624[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_376 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_62[0][0]         
                                                                 tf_op_layer_Reshape_376/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_376/multiples  [(3,)]               0           tf_op_layer_strided_slice_628[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_377 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_377/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_377/multiples  [(3,)]               0           tf_op_layer_strided_slice_627[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_372 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_372[0][0]    
                                                                 tf_op_layer_Tile_372/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_373 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_373[0][0]    
                                                                 tf_op_layer_Tile_373/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_374 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_374[0][0]    
                                                                 tf_op_layer_Tile_374/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_375 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_375[0][0]    
                                                                 tf_op_layer_Tile_375/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_376 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_376[0][0]    
                                                                 tf_op_layer_Tile_376/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_377 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_377[0][0]    
                                                                 tf_op_layer_Tile_377/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_310 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_372[0][0]       
                                                                 tf_op_layer_Tile_373[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_311 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_374[0][0]       
                                                                 tf_op_layer_Tile_375[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_312 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_376[0][0]       
                                                                 tf_op_layer_Tile_377[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_248 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_310[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_249 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_311[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_250 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_312[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_434 (TensorFlo [(None, None)]       0           tf_op_layer_Square_248[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_435 (TensorFlo [(None, None)]       0           tf_op_layer_Square_249[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_436 (TensorFlo [(None, None)]       0           tf_op_layer_Square_250[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_186 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_434[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_186 (TensorFlo [()]                 0           tf_op_layer_strided_slice_623[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_187 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_435[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_187 (TensorFlo [()]                 0           tf_op_layer_strided_slice_626[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_188 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_436[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_188 (TensorFlo [()]                 0           tf_op_layer_strided_slice_629[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_186 (Tensor [(None, None)]       0           tf_op_layer_Neg_186[0][0]        
                                                                 tf_op_layer_Cast_186[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_187 (Tensor [(None, None)]       0           tf_op_layer_Neg_187[0][0]        
                                                                 tf_op_layer_Cast_187[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_188 (Tensor [(None, None)]       0           tf_op_layer_Neg_188[0][0]        
                                                                 tf_op_layer_Cast_188[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_186 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_186[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_187 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_187[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_188 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_188[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_437 (TensorFlo [()]                 0           tf_op_layer_Exp_186[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_438 (TensorFlo [()]                 0           tf_op_layer_Exp_187[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_439 (TensorFlo [()]                 0           tf_op_layer_Exp_188[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_314 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_124 (TensorFl [()]                 0           tf_op_layer_Mean_437[0][0]       
                                                                 tf_op_layer_Mean_438[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_187 (TensorFlow [()]                 0           tf_op_layer_Mean_439[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_251 (TensorF [(None, 97)]         0           tf_op_layer_Sub_314[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_313 (TensorFlow [()]                 0           tf_op_layer_AddV2_124[0][0]      
                                                                 tf_op_layer_Mul_187[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_62 (TensorFlowO [(None,)]            0           tf_op_layer_Square_251[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_188 (TensorFlow [()]                 0           tf_op_layer_Sub_313[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_125 (TensorFl [(None,)]            0           tf_op_layer_Sum_62[0][0]         
                                                                 tf_op_layer_Mul_188[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_440 (TensorFlo [()]                 0           tf_op_layer_AddV2_125[0][0]      
__________________________________________________________________________________________________
add_loss_62 (AddLoss)           ()                   0           tf_op_layer_Mean_440[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
--------------------------------------------------------------------------------
                             Loaded saved VAE :) 	                              
--------------------------------------------------------------------------------

4) Setup data for visualisations

Here we setup the data and summarise key features, for example calcualting the median values.

We do this so we can easily access these later.

In [9]:
# -----------------------------------------------------------------------------------
#                         Data setup for Vis
# -----------------------------------------------------------------------------------

hist_metric = 'signal'
input_cols = list(df_input.columns)


def get_median(df, mark, time="", tissue="brain"):
    cols = []
    for c in df.columns:
        if tissue in c and mark in c and hist_metric in c and time in c and 'median' not in c:
            cols.append(c)
    # get nan median
    vals = np.nanmedian(df[cols].values, axis=1)
    
    return np.nan_to_num(vals)

marks = ['H3K27me3', 'H3K4me1', 'H3K4me2', 'H3K4me3', 'H3K27ac', 'H3K9me3', 'H3K36me3', 'H3K9ac']

# Get the median over all time points and all brain tissues
for m in marks:
    df[f'{m}'] = get_median(df, m, time="", tissue="brain")
    
times = ['10.5', '11.5', '12.5', '13.5', '14.5', '15.5', '16.5']
for m in marks:
    for e in times:
        df[f'median_brain_{e}_signal_{m}'] = get_median(df, m, time=e, tissue="brain")
    
tissues = ['hindbrain', 'midbrain', 'forebrain']
for m in marks:
    for e in times:
        for t in tissues:
            df[f'median_{t}_{e}_signal_{m}'] = get_median(df, m, time=e, tissue=t)


df['mean_wt'] = np.mean(df[[c for c in df.columns if 'wt' in c]].values, axis=1)
df['mean_ko'] = np.mean(df[[c for c in df.columns if 'ko' in c]].values, axis=1)
df['mean_ko-wt'] = df['mean_ko'].values - df['mean_wt'].values

# Add in the VAE columns 
for v in range(0, len(vae_data[0])):
    df[f'VAE{v}'] = vae_data[:, v]

# Rename/add some columns just to make figs easier
df['Log2FC FB'] = df['log2FoldChange_fb'].values
df['Log2FC MB'] = df['log2FoldChange_mb'].values
df['Log2FC HB'] = df['log2FoldChange_hb'].values
df['Log2FC SC'] = df['log2FoldChange_sc'].values
df['Log2FC A11'] = df['log2FoldChange_a11'].values
df['Log2FC A13'] = df['log2FoldChange_a13'].values
df['Log2FC A15'] = df['log2FoldChange_a15'].values
df['Log2FC A18'] = df['log2FoldChange_a18'].values
df['Log2FC P11'] = df['log2FoldChange_p11'].values
df['Log2FC P13'] = df['log2FoldChange_p13'].values
df['Log2FC P15'] = df['log2FoldChange_p15'].values
df['Log2FC P18'] = df['log2FoldChange_p18'].values

all_cols = [
    'H3K9me3',
    'H3K4me1', 
    'H3K4me2', 
    'H3K4me3', 
    'H3K27ac',
    'H3K36me3', 
    'H3K9ac',
    'mean_wt',
    'mean_ko',
    'mean_ko-wt',
    'H3K27me3',
    'Log2FC FB',
    'Log2FC MB',
    'Log2FC HB',
    'Log2FC SC',
    'VAE0',
    'VAE1',
    'VAE2',
    'Log2FC A11',
    'Log2FC A13',
    'Log2FC A15',
    'Log2FC A18',
    'Log2FC P11',
    'Log2FC P13',
    'Log2FC P15',
    'Log2FC P18'
]
unobserved_cols = [
    'H3K9me3',
    'H3K4me1', 
    'H3K4me2', 
    'H3K4me3', 
    'H3K27ac',
    'H3K36me3', 
    'H3K9ac',
    'mean_wt',
    'mean_ko',
    'mean_ko-wt'
]

observed_cols = [
    'H3K4me1', 
    'H3K4me2', 
    'H3K4me3', 
    'H3K27ac',
    'H3K36me3', 
    'H3K27me3',
    'Log2FC FB',
    'Log2FC MB',
    'Log2FC HB',
    'Log2FC SC',
    'VAE0',
    'VAE1',
    'VAE2'
]

hist_cols = [
    'H3K9me3',
    'H3K4me1', 
    'H3K4me2', 
    'H3K4me3', 
    'H3K27ac',
    'H3K36me3', 
    'H3K27me3',
    'H3K9ac'
]

div_cols = [
    'Log2FC FB',
    'Log2FC MB',
    'Log2FC HB',
    'Log2FC SC',
    'Log2FC A11',
    'Log2FC A13',
    'Log2FC A15',
    'Log2FC A18',
    'Log2FC P11',
    'Log2FC P13',
    'Log2FC P15',
    'Log2FC P18',
]

raw_cols = [
    'mean_wt',
    'mean_ko'
]
vae_cols = observed_cols
df_unobserved = df[unobserved_cols]
df_observed = df[observed_cols]
observed_dict = {}

for c in observed_cols:
    observed_dict[c] = df_observed[c].values
unobserved_dict = {}
for c in unobserved_cols:
    unobserved_dict[c] = df_unobserved[c].values
    
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1113: RuntimeWarning: Mean of empty slice
  return np.nanmean(a, axis, out=out, keepdims=keepdims)

4) Plot the correlations for the latent nodes with data features

Here we want to plot how correlated each node is with the mean values of different epigenetic marks & also the means of input features in the dataset.

We also plot the latent space coloured by each of the features (both observed and unobserved).

Lastly, we plot heatmaps of the input, latent and decoding results for key marker genes so that we can work out whether our model is logical. These same genes are also projected onto the latent space where we can see that the different classes of markers (e.g. Hox genes vs brain genes) are easily discernable.

In [10]:
# -----------------------------------------------------------------------------------
#                         Plot scatter of the features
# -----------------------------------------------------------------------------------
all_vae_data = np.copy(vae_data)
vae_data = all_vae_data[:, 0:3]
vae_vis = VVis(vae_mse, u, None)
div_cmap = 'seismic'

feature_obs_ax = vae_vis.plot_feature_scatters(df, "", columns=hist_cols, vae_data=vae_data, 
                                               show_plt=False, output_dir=fig_dir, fig_type="svg", 
                                               save_fig=False, title=f'{experiment_name}',
                                               angle_plot=315, cmap=hist_cmap, vmin=0, vmax=10)

feature_obs_ax = vae_vis.plot_feature_scatters(df, "", columns=div_cols, vae_data=vae_data, 
                                               show_plt=False, output_dir=fig_dir, fig_type="svg", 
                                               save_fig=False, title=f'{experiment_name}',
                                               angle_plot=315, cmap=div_cmap, vmin=-10, vmax=10)

feature_obs_ax = vae_vis.plot_feature_scatters(df, "", columns=raw_cols, vae_data=vae_data, 
                                               show_plt=False, output_dir=fig_dir, fig_type="svg", 
                                               save_fig=False, title=f'{experiment_name}',
                                               angle_plot=315, cmap=rna_cmap, vmin=0, vmax=10)

# -----------------------------------------------------------------------------------
#                         Plot histograms
# -----------------------------------------------------------------------------------
vae_vis.plot_node_hists(show_plt=True, save_fig=False)
vae_vis.plot_input_distribution(df_observed, show_plt=True, save_fig=True, output_dir=fig_dir)
vae_vis.plot_input_distribution(df_unobserved, show_plt=True, output_dir=fig_dir, save_fig=True)

# -----------------------------------------------------------------------------------
#                         Plot correlations
# -----------------------------------------------------------------------------------
vae_vis.plot_node_correlation(show_plt=True, save_fig=False)

vae_vis.plot_node_feature_correlation(df_observed, '', columns=observed_cols, vae_data=vae_data, 
                                  show_plt=True, output_dir=fig_dir, save_fig=True, 
                                      title=f'Node vs observed features {experiment_name}', cmap=div_cmap)

vae_vis.plot_feature_correlation(df_observed, '', columns=observed_cols, show_plt=True, cmap=div_cmap,
                                 title=f'Feature vs observed features {experiment_name}',
                             output_dir=fig_dir, save_fig=True)

vae_vis.plot_node_feature_correlation(df_unobserved, '', columns=unobserved_cols, vae_data=vae_data, 
                                      cmap=div_cmap,
                                      show_plt=True, output_dir=fig_dir, save_fig=True,
                                      title=f'Node vs unobserved features {experiment_name}')

vae_vis.plot_feature_correlation(df_unobserved, '', columns=unobserved_cols, show_plt=True, cmap=div_cmap,
                             output_dir=fig_dir, save_fig=True, title=f'feature un-observed features {experiment_name}')

vae_vis.plot_feature_correlation(df, '', columns=unobserved_cols + observed_cols, show_plt=True, cmap=div_cmap,
                             output_dir=fig_dir, save_fig=True, title=f'features all {experiment_name}')
        
vae_vis.plot_feature_correlation(df, '', columns=vae_cols, show_plt=True, cmap=div_cmap,
                             output_dir=fig_dir, save_fig=True, title=f'features VAE {experiment_name}')
# -----------------------------------------------------------------------------------
#                         Plot heatmaps of marker genes
# -----------------------------------------------------------------------------------
fb_genes = ['Foxg1']
mb_genes = ['En2']
hb_genes = ['Sox3']
sc_genes = ['Hoxc9']
input_cols = [c for c in df_input.columns if c != gene_name]

merged_cols = [c for c in df.columns if "merged" in c]
# Plot the input space
plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes, 
                  merged_cols, vae_data, vae_mse,
                  gene_name, mark='H3K27me3', method='input', cmap=hist_cmap)

# Plot the decoding space
plot_gene_heatmap(df_input, fb_genes, mb_genes, hb_genes, sc_genes, 
                  input_cols, vae_data, vae_mse, 
                  gene_name, mark='H3K27me3', method='decoding', cmap=div_cmap)
bin_cmap = 'bone'
# Plot the latent space
plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes, 
                  ['VAE0', 'VAE1', 'VAE2'], vae_data, vae_mse, 
                  gene_name, method='latent', cmap=bin_cmap, mark="H3K27me3")

# Also do the main markers
fb_genes = ['Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6']
mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4']
hb_genes = ['Phox2b', 'Krox20', 'Fev', 'Hoxb1',  'Hoxd3']
sc_genes = ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11','Hoxa7', 'Hoxa9', 'Hoxa10',
              'Hoxb9', 'Hoxb13',  'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13']
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/scivae/vis.py:351: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  fig = plt.figure()
<Figure size 216x216 with 0 Axes>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>
1 1 1.0 0.0
1 2 -0.04487386126874613 0.09674029345160055
1 3 -0.06714065131052843 0.012898604337748273
2 1 -0.04487386126874613 0.09674029345160055
2 2 1.0 0.0
2 3 -0.010003465908807808 0.7113303992704973
3 1 -0.06714065131052843 0.012898604337748273
3 2 -0.010003465908807808 0.7113303992704973
3 3 1.0 0.0
<Figure size 216x216 with 0 Axes>
1 H3K4me1 0.29031611063142665 4.930166724775107e-28
1 H3K4me2 0.4130760641843135 1.255080109427997e-57
1 H3K4me3 0.5049970332540886 1.2931183112620578e-89
1 H3K27ac 0.565460701897515 1.1067285645987936e-116
1 H3K36me3 0.5128226801227292 8.11907865775757e-93
1 H3K27me3 0.03803275569023637 0.15928977898633345
1 Log2FC FB -0.3565934229558133 2.2360651454584692e-42
1 Log2FC MB -0.41092267559995416 5.448112954456422e-57
1 Log2FC HB -0.33584075926758683 1.696244835667772e-37
1 Log2FC SC -0.30086852336773884 4.42036845192071e-30
1 VAE0 1.0 0.0
1 VAE1 -0.044440792633740046 0.10000658140919086
1 VAE2 -0.06607105313289889 0.014410819464438108
2 H3K4me1 -0.42943166638024277 1.2627522245545507e-62
2 H3K4me2 -0.3459967816690149 7.715291848745455e-40
2 H3K4me3 -0.29554922126419003 4.8781949636658434e-29
2 H3K27ac -0.13974158340089593 2.0471583971385752e-07
2 H3K36me3 0.0469662393901308 0.08214161188440466
2 H3K27me3 -0.9347886653056977 0.0
2 Log2FC FB -0.4254659838623871 2.180071569852324e-61
2 Log2FC MB -0.23624776872255862 7.626021772616817e-19
2 Log2FC HB 0.0008937253954734859 0.9736252988078797
2 Log2FC SC -0.19659919987030305 2.0632386010018648e-13
2 VAE0 -0.044440792633740046 0.10000658140919086
2 VAE1 1.0 0.0
2 VAE2 -0.010831726378816332 0.6886318434581902
3 H3K4me1 0.1608669464307792 2.097868335239766e-09
3 H3K4me2 0.0547856481985845 0.042537576997147844
3 H3K4me3 0.08246692173925642 0.0022436715756004757
3 H3K27ac 0.02811476348996577 0.29821822708243745
3 H3K36me3 0.016248697834837858 0.5477522547037476
3 H3K27me3 0.06358119033023919 0.01854979231058591
3 Log2FC FB -0.2673799783130146 7.108362262493199e-24
3 Log2FC MB -0.02289057494832727 0.39704663424441755
3 Log2FC HB 0.16871385420091436 3.2493899832182813e-10
3 Log2FC SC 0.0544089035764286 0.043982879996444985
3 VAE0 -0.06607105313289889 0.014410819464438108
3 VAE1 -0.010831726378816332 0.6886318434581902
3 VAE2 1.0 0.0
<Figure size 144x144 with 0 Axes>
H3K4me1 H3K4me1 1.0 0.0
H3K4me1 H3K4me2 0.5778097092631771 6.30995115822728e-123
H3K4me1 H3K4me3 0.5424054244750087 1.0462500992593941e-105
H3K4me1 H3K27ac 0.4474397884575597 1.868776646979828e-68
H3K4me1 H3K36me3 0.23331708205528487 2.0919177335021665e-18
H3K4me1 H3K27me3 0.4183583143974251 3.2720909943747853e-59
H3K4me1 Log2FC FB -0.005828660227440098 0.829281719129814
H3K4me1 Log2FC MB -0.07363958460652882 0.006374445946102697
H3K4me1 Log2FC HB -0.12856724647180287 1.7885836117251365e-06
H3K4me1 Log2FC SC 0.01896101107434295 0.48299579643865487
H3K4me1 VAE0 0.29031611063142665 4.930166724775107e-28
H3K4me1 VAE1 -0.4294316663802427 1.2627522245545507e-62
H3K4me1 VAE2 0.1608669464307792 2.097868335239766e-09
H3K4me2 H3K4me1 0.5778097092631771 6.30995115822728e-123
H3K4me2 H3K4me2 1.0 0.0
H3K4me2 H3K4me3 0.9249576243822666 0.0
H3K4me2 H3K27ac 0.5809337496355107 1.505979846743912e-124
H3K4me2 H3K36me3 0.2978871153402344 1.7085900450507975e-29
H3K4me2 H3K27me3 0.32314796735960055 1.0851872748676937e-34
H3K4me2 Log2FC FB -0.09022151932051348 0.0008245804102662129
H3K4me2 Log2FC MB -0.18060329215713622 1.6216971012303665e-11
H3K4me2 Log2FC HB -0.2789470609848839 6.36148954888253e-26
H3K4me2 Log2FC SC -0.08115312156234512 0.0026377199644407583
H3K4me2 VAE0 0.41307606418431353 1.255080109427997e-57
H3K4me2 VAE1 -0.3459967816690149 7.715291848745455e-40
H3K4me2 VAE2 0.0547856481985845 0.042537576997147844
H3K4me3 H3K4me1 0.5424054244750087 1.0462500992593941e-105
H3K4me3 H3K4me2 0.9249576243822665 0.0
H3K4me3 H3K4me3 1.0 0.0
H3K4me3 H3K27ac 0.6651601396750653 7.046953869800731e-176
H3K4me3 H3K36me3 0.38465069892378145 1.4060008082927108e-49
H3K4me3 H3K27me3 0.28080152722934676 2.9236513393994367e-26
H3K4me3 Log2FC FB -0.1552552713147644 7.538405719288966e-09
H3K4me3 Log2FC MB -0.25008860580419645 5.3855237481581464e-21
H3K4me3 Log2FC HB -0.33927820537868725 2.7958816066164293e-38
H3K4me3 Log2FC SC -0.1462532917403755 5.338305069703614e-08
H3K4me3 VAE0 0.5049970332540887 1.2931183112619104e-89
H3K4me3 VAE1 -0.29554922126419003 4.8781949636658434e-29
H3K4me3 VAE2 0.0824669217392564 0.0022436715756004757
H3K27ac H3K4me1 0.44743978845755966 1.868776646980095e-68
H3K27ac H3K4me2 0.5809337496355107 1.505979846743912e-124
H3K27ac H3K4me3 0.6651601396750653 7.046953869800731e-176
H3K27ac H3K27ac 1.0 0.0
H3K27ac H3K36me3 0.40924313296843956 1.699390424794447e-56
H3K27ac H3K27me3 0.1335242473543216 6.986885390846781e-07
H3K27ac Log2FC FB -0.1895952937422755 1.462114117086756e-12
H3K27ac Log2FC MB -0.27333105823559734 6.462122594893282e-25
H3K27ac Log2FC HB -0.34523770917786767 1.162608260691108e-39
H3K27ac Log2FC SC -0.19369439965896984 4.689838803083501e-13
H3K27ac VAE0 0.565460701897515 1.1067285645987936e-116
H3K27ac VAE1 -0.13974158340089593 2.0471583971385752e-07
H3K27ac VAE2 0.02811476348996577 0.29821822708243745
H3K36me3 H3K4me1 0.23331708205528484 2.0919177335021665e-18
H3K36me3 H3K4me2 0.2978871153402344 1.7085900450507975e-29
H3K36me3 H3K4me3 0.38465069892378145 1.4060008082927108e-49
H3K36me3 H3K27ac 0.40924313296843967 1.6993904247943983e-56
H3K36me3 H3K36me3 1.0 0.0
H3K36me3 H3K27me3 -0.060811738887076326 0.024340755777373508
H3K36me3 Log2FC FB -0.22787654840262245 1.3131908798832538e-17
H3K36me3 Log2FC MB -0.29142012227610314 3.038589462812012e-28
H3K36me3 Log2FC HB -0.27428074788659457 4.382946347360937e-25
H3K36me3 Log2FC SC -0.23820189269891243 3.861529498749434e-19
H3K36me3 VAE0 0.5128226801227292 8.11907865775757e-93
H3K36me3 VAE1 0.04696623939013081 0.08214161188440461
H3K36me3 VAE2 0.016248697834837858 0.5477522547037476
H3K27me3 H3K4me1 0.4183583143974251 3.2720909943747853e-59
H3K27me3 H3K4me2 0.32314796735960055 1.0851872748676937e-34
H3K27me3 H3K4me3 0.28080152722934676 2.9236513393994367e-26
H3K27me3 H3K27ac 0.13352424735432158 6.986885390846871e-07
H3K27me3 H3K36me3 -0.060811738887076326 0.024340755777373508
H3K27me3 H3K27me3 1.0 0.0
H3K27me3 Log2FC FB 0.34922344775478703 1.3330712289599865e-40
H3K27me3 Log2FC MB 0.21994143742472344 1.7596945962900848e-16
H3K27me3 Log2FC HB 0.05577417477278321 0.03893482554600838
H3K27me3 Log2FC SC 0.2297418333448438 7.032519975432944e-18
H3K27me3 VAE0 0.03803275569023637 0.15928977898633345
H3K27me3 VAE1 -0.9347886653056977 0.0
H3K27me3 VAE2 0.0635811903302392 0.018549792310585875
Log2FC FB H3K4me1 -0.005828660227440098 0.829281719129814
Log2FC FB H3K4me2 -0.09022151932051346 0.0008245804102662144
Log2FC FB H3K4me3 -0.1552552713147644 7.538405719288966e-09
Log2FC FB H3K27ac -0.1895952937422755 1.462114117086756e-12
Log2FC FB H3K36me3 -0.22787654840262248 1.3131908798832538e-17
Log2FC FB H3K27me3 0.349223447754787 1.3330712289599865e-40
Log2FC FB Log2FC FB 1.0 0.0
Log2FC FB Log2FC MB 0.4874154790987053 1.0212500251884752e-82
Log2FC FB Log2FC HB 0.2538593319769123 1.323350676401945e-21
Log2FC FB Log2FC SC 0.3847565054541962 1.316627432201345e-49
Log2FC FB VAE0 -0.35659342295581337 2.2360651454584055e-42
Log2FC FB VAE1 -0.42546598386238704 2.180071569852324e-61
Log2FC FB VAE2 -0.2673799783130145 7.108362262493554e-24
Log2FC MB H3K4me1 -0.07363958460652881 0.0063744459461027095
Log2FC MB H3K4me2 -0.18060329215713622 1.6216971012303665e-11
Log2FC MB H3K4me3 -0.2500886058041965 5.385523748158109e-21
Log2FC MB H3K27ac -0.27333105823559734 6.462122594893282e-25
Log2FC MB H3K36me3 -0.29142012227610314 3.038589462812012e-28
Log2FC MB H3K27me3 0.2199414374247234 1.7596945962901607e-16
Log2FC MB Log2FC FB 0.48741547909870536 1.0212500251884752e-82
Log2FC MB Log2FC MB 1.0 0.0
Log2FC MB Log2FC HB 0.5825485565927759 2.1497777521472687e-125
Log2FC MB Log2FC SC 0.41836646559213686 3.253563739871815e-59
Log2FC MB VAE0 -0.4109226755999542 5.448112954456422e-57
Log2FC MB VAE1 -0.23624776872255865 7.626021772616763e-19
Log2FC MB VAE2 -0.02289057494832727 0.39704663424441755
Log2FC HB H3K4me1 -0.1285672464718029 1.788583611725127e-06
Log2FC HB H3K4me2 -0.278947060984884 6.361489548882348e-26
Log2FC HB H3K4me3 -0.33927820537868725 2.7958816066164293e-38
Log2FC HB H3K27ac -0.34523770917786767 1.162608260691108e-39
Log2FC HB H3K36me3 -0.27428074788659457 4.382946347360937e-25
Log2FC HB H3K27me3 0.05577417477278321 0.03893482554600838
Log2FC HB Log2FC FB 0.2538593319769123 1.323350676401945e-21
Log2FC HB Log2FC MB 0.5825485565927759 2.1497777521472687e-125
Log2FC HB Log2FC HB 1.0 0.0
Log2FC HB Log2FC SC 0.4916008496130506 2.532062750739362e-84
Log2FC HB VAE0 -0.3358407592675868 1.696244835667893e-37
Log2FC HB VAE1 0.000893725395473486 0.9736252988078797
Log2FC HB VAE2 0.16871385420091436 3.2493899832182813e-10
Log2FC SC H3K4me1 0.01896101107434295 0.48299579643865487
Log2FC SC H3K4me2 -0.08115312156234512 0.0026377199644407583
Log2FC SC H3K4me3 -0.1462532917403755 5.338305069703614e-08
Log2FC SC H3K27ac -0.19369439965896987 4.689838803083501e-13
Log2FC SC H3K36me3 -0.23820189269891243 3.861529498749434e-19
Log2FC SC H3K27me3 0.2297418333448438 7.032519975432944e-18
Log2FC SC Log2FC FB 0.38475650545419626 1.3166274322012883e-49
Log2FC SC Log2FC MB 0.4183664655921368 3.253563739871815e-59
Log2FC SC Log2FC HB 0.4916008496130506 2.532062750739362e-84
Log2FC SC Log2FC SC 1.0 0.0
Log2FC SC VAE0 -0.30086852336773884 4.42036845192071e-30
Log2FC SC VAE1 -0.19659919987030303 2.0632386010018646e-13
Log2FC SC VAE2 0.0544089035764286 0.043982879996444985
VAE0 H3K4me1 0.29031611063142665 4.930166724775107e-28
VAE0 H3K4me2 0.4130760641843135 1.255080109427997e-57
VAE0 H3K4me3 0.5049970332540886 1.2931183112620578e-89
VAE0 H3K27ac 0.565460701897515 1.1067285645987936e-116
VAE0 H3K36me3 0.5128226801227292 8.11907865775757e-93
VAE0 H3K27me3 0.03803275569023637 0.15928977898633345
VAE0 Log2FC FB -0.3565934229558133 2.2360651454584692e-42
VAE0 Log2FC MB -0.41092267559995416 5.448112954456422e-57
VAE0 Log2FC HB -0.33584075926758683 1.696244835667772e-37
VAE0 Log2FC SC -0.30086852336773884 4.42036845192071e-30
VAE0 VAE0 1.0 0.0
VAE0 VAE1 -0.044440792633740046 0.10000658140919086
VAE0 VAE2 -0.06607105313289889 0.014410819464438108
VAE1 H3K4me1 -0.42943166638024277 1.2627522245545507e-62
VAE1 H3K4me2 -0.3459967816690149 7.715291848745455e-40
VAE1 H3K4me3 -0.29554922126419003 4.8781949636658434e-29
VAE1 H3K27ac -0.13974158340089593 2.0471583971385752e-07
VAE1 H3K36me3 0.0469662393901308 0.08214161188440466
VAE1 H3K27me3 -0.9347886653056977 0.0
VAE1 Log2FC FB -0.4254659838623871 2.180071569852324e-61
VAE1 Log2FC MB -0.23624776872255862 7.626021772616817e-19
VAE1 Log2FC HB 0.0008937253954734859 0.9736252988078797
VAE1 Log2FC SC -0.19659919987030305 2.0632386010018648e-13
VAE1 VAE0 -0.044440792633740046 0.10000658140919086
VAE1 VAE1 1.0 0.0
VAE1 VAE2 -0.010831726378816332 0.6886318434581902
VAE2 H3K4me1 0.1608669464307792 2.097868335239766e-09
VAE2 H3K4me2 0.0547856481985845 0.042537576997147844
VAE2 H3K4me3 0.08246692173925642 0.0022436715756004757
VAE2 H3K27ac 0.02811476348996577 0.29821822708243745
VAE2 H3K36me3 0.016248697834837858 0.5477522547037476
VAE2 H3K27me3 0.06358119033023919 0.01854979231058591
VAE2 Log2FC FB -0.2673799783130146 7.108362262493199e-24
VAE2 Log2FC MB -0.02289057494832727 0.39704663424441755
VAE2 Log2FC HB 0.16871385420091436 3.2493899832182813e-10
VAE2 Log2FC SC 0.0544089035764286 0.043982879996444985
VAE2 VAE0 -0.06607105313289889 0.014410819464438108
VAE2 VAE1 -0.010831726378816332 0.6886318434581902
VAE2 VAE2 1.0 0.0
<Figure size 216x216 with 0 Axes>
1 H3K9me3 0.03537250227723582 0.19055019655164637
1 H3K4me1 0.29031611063142665 4.930166724775107e-28
1 H3K4me2 0.4130760641843135 1.255080109427997e-57
1 H3K4me3 0.5049970332540886 1.2931183112620578e-89
1 H3K27ac 0.565460701897515 1.1067285645987936e-116
1 H3K36me3 0.5128226801227292 8.11907865775757e-93
1 H3K9ac 0.5039806392576622 3.322369405336695e-89
1 mean_wt 0.9225507496267616 0.0
1 mean_ko 0.8281568932120574 0.0
1 mean_ko-wt -0.012947891180987355 0.6319353745642039
2 H3K9me3 -0.011191335427176704 0.6788618286935884
2 H3K4me1 -0.42943166638024277 1.2627522245545507e-62
2 H3K4me2 -0.3459967816690149 7.715291848745455e-40
2 H3K4me3 -0.29554922126419003 4.8781949636658434e-29
2 H3K27ac -0.13974158340089593 2.0471583971385752e-07
2 H3K36me3 0.0469662393901308 0.08214161188440466
2 H3K9ac -0.25411824491596513 1.2007357717167866e-21
2 mean_wt -0.0843331888079318 0.0017760748791830353
2 mean_ko -0.1975228461039166 1.5848762933272344e-13
2 mean_ko-wt -0.3364180123508915 1.2551528216037472e-37
3 H3K9me3 -0.055276325015001807 0.04071553265967369
3 H3K4me1 0.1608669464307792 2.097868335239766e-09
3 H3K4me2 0.0547856481985845 0.042537576997147844
3 H3K4me3 0.08246692173925642 0.0022436715756004757
3 H3K27ac 0.02811476348996577 0.29821822708243745
3 H3K36me3 0.016248697834837858 0.5477522547037476
3 H3K9ac -0.0071413713299010246 0.7916367064605488
3 mean_wt -0.3780081450486794 8.274659191134132e-48
3 mean_ko -0.46508046817661947 1.6241298929221246e-74
3 mean_ko-wt -0.3786089351715797 5.746004360247138e-48
<Figure size 144x144 with 0 Axes>
H3K9me3 H3K9me3 1.0 0.0
H3K9me3 H3K4me1 0.007933317702121464 0.7691523998107841
H3K9me3 H3K4me2 0.044228672221843755 0.10163817786462813
H3K9me3 H3K4me3 0.053737798650920314 0.04666004836886921
H3K9me3 H3K27ac 0.03718373706351468 0.16881454156123957
H3K9me3 H3K36me3 0.06906636672694122 0.010526278474959678
H3K9me3 H3K9ac 0.040046841077399725 0.13832510654544702
H3K9me3 mean_wt 0.04307736823791785 0.11086624765467276
H3K9me3 mean_ko 0.04734098647425971 0.07972581975723912
H3K9me3 mean_ko-wt 0.0348065516761931 0.1977468275954522
H3K4me1 H3K9me3 0.007933317702121464 0.7691523998107841
H3K4me1 H3K4me1 1.0 0.0
H3K4me1 H3K4me2 0.5778097092631771 6.30995115822728e-123
H3K4me1 H3K4me3 0.5424054244750087 1.0462500992593941e-105
H3K4me1 H3K27ac 0.4474397884575597 1.868776646979828e-68
H3K4me1 H3K36me3 0.23331708205528487 2.0919177335021665e-18
H3K4me1 H3K9ac 0.49904269037186083 3.110616458783235e-87
H3K4me1 mean_wt 0.24278998959573111 7.627059760649075e-20
H3K4me1 mean_ko 0.23956359735363475 2.3946366296042235e-19
H3K4me1 mean_ko-wt 0.025077000286833816 0.35349809962892376
H3K4me2 H3K9me3 0.04422867222184375 0.1016381778646282
H3K4me2 H3K4me1 0.5778097092631771 6.30995115822728e-123
H3K4me2 H3K4me2 1.0 0.0
H3K4me2 H3K4me3 0.9249576243822666 0.0
H3K4me2 H3K27ac 0.5809337496355107 1.505979846743912e-124
H3K4me2 H3K36me3 0.2978871153402344 1.7085900450507975e-29
H3K4me2 H3K9ac 0.8627487422150418 0.0
H3K4me2 mean_wt 0.3900660554177981 4.727264630671039e-51
H3K4me2 mean_ko 0.35797046417790596 1.0292297432585097e-42
H3K4me2 mean_ko-wt -0.02879671157287191 0.28664758486556835
H3K4me3 H3K9me3 0.053737798650920314 0.04666004836886921
H3K4me3 H3K4me1 0.5424054244750087 1.0462500992593941e-105
H3K4me3 H3K4me2 0.9249576243822665 0.0
H3K4me3 H3K4me3 1.0 0.0
H3K4me3 H3K27ac 0.6651601396750653 7.046953869800731e-176
H3K4me3 H3K36me3 0.38465069892378145 1.4060008082927108e-49
H3K4me3 H3K9ac 0.9030038842756943 0.0
H3K4me3 mean_wt 0.47415516734627033 8.943496244161752e-78
H3K4me3 mean_ko 0.4341695065699847 3.994694583459608e-64
H3K4me3 mean_ko-wt -0.0951739358222856 0.0004174400224976428
H3K27ac H3K9me3 0.03718373706351468 0.16881454156123957
H3K27ac H3K4me1 0.44743978845755966 1.868776646980095e-68
H3K27ac H3K4me2 0.5809337496355107 1.505979846743912e-124
H3K27ac H3K4me3 0.6651601396750653 7.046953869800731e-176
H3K27ac H3K27ac 1.0 0.0
H3K27ac H3K36me3 0.40924313296843956 1.699390424794447e-56
H3K27ac H3K9ac 0.7227237508904014 6.882193462599804e-222
H3K27ac mean_wt 0.529031154228644 1.0127317607575462e-99
H3K27ac mean_ko 0.46561586007210876 1.0496706851591415e-74
H3K27ac mean_ko-wt -0.10802112651026943 6.129116274337846e-05
H3K36me3 H3K9me3 0.0690663667269412 0.010526278474959691
H3K36me3 H3K4me1 0.23331708205528484 2.0919177335021665e-18
H3K36me3 H3K4me2 0.2978871153402344 1.7085900450507975e-29
H3K36me3 H3K4me3 0.38465069892378145 1.4060008082927108e-49
H3K36me3 H3K27ac 0.40924313296843967 1.6993904247943983e-56
H3K36me3 H3K36me3 1.0 0.0
H3K36me3 H3K9ac 0.3916349805435466 1.7480657850104863e-51
H3K36me3 mean_wt 0.4861825101262901 3.0054313008082013e-82
H3K36me3 mean_ko 0.4231395387694597 1.1391534566952996e-60
H3K36me3 mean_ko-wt -0.15668201024884568 5.469081652632577e-09
H3K9ac H3K9me3 0.040046841077399725 0.13832510654544702
H3K9ac H3K4me1 0.49904269037186083 3.110616458783235e-87
H3K9ac H3K4me2 0.8627487422150417 0.0
H3K9ac H3K4me3 0.9030038842756942 0.0
H3K9ac H3K27ac 0.7227237508904014 6.882193462599804e-222
H3K9ac H3K36me3 0.3916349805435467 1.7480657850104365e-51
H3K9ac H3K9ac 0.9999999999999999 0.0
H3K9ac mean_wt 0.49764003406030627 1.1141987545204845e-86
H3K9ac mean_ko 0.44585615791285194 6.286794883249051e-68
H3K9ac mean_ko-wt -0.08196167319953047 0.002388351628186672
mean_wt H3K9me3 0.04307736823791785 0.11086624765467276
mean_wt H3K4me1 0.24278998959573111 7.627059760649075e-20
mean_wt H3K4me2 0.39006605541779815 4.7272646306710386e-51
mean_wt H3K4me3 0.4741551673462703 8.943496244162007e-78
mean_wt H3K27ac 0.529031154228644 1.0127317607575462e-99
mean_wt H3K36me3 0.4861825101262901 3.0054313008082013e-82
mean_wt H3K9ac 0.49764003406030627 1.1141987545204845e-86
mean_wt mean_wt 1.0 0.0
mean_wt mean_ko 0.9286957551860459 0.0
mean_wt mean_ko-wt 0.04740020285089276 0.07934941563683097
mean_ko H3K9me3 0.04734098647425971 0.07972581975723912
mean_ko H3K4me1 0.23956359735363478 2.3946366296042235e-19
mean_ko H3K4me2 0.357970464177906 1.0292297432585098e-42
mean_ko H3K4me3 0.43416950656998476 3.994694583459039e-64
mean_ko H3K27ac 0.4656158600721088 1.0496706851591115e-74
mean_ko H3K36me3 0.4231395387694597 1.1391534566952996e-60
mean_ko H3K9ac 0.44585615791285194 6.286794883249051e-68
mean_ko mean_wt 0.9286957551860459 0.0
mean_ko mean_ko 1.0 0.0
mean_ko mean_ko-wt 0.32128358579954336 2.732292140596804e-34
mean_ko-wt H3K9me3 0.0348065516761931 0.1977468275954522
mean_ko-wt H3K4me1 0.025077000286833816 0.35349809962892376
mean_ko-wt H3K4me2 -0.02879671157287191 0.28664758486556835
mean_ko-wt H3K4me3 -0.0951739358222856 0.0004174400224976428
mean_ko-wt H3K27ac -0.10802112651026943 6.129116274337846e-05
mean_ko-wt H3K36me3 -0.15668201024884568 5.469081652632577e-09
mean_ko-wt H3K9ac -0.08196167319953047 0.002388351628186672
mean_ko-wt mean_wt 0.04740020285089276 0.07934941563683097
mean_ko-wt mean_ko 0.32128358579954336 2.732292140596804e-34
mean_ko-wt mean_ko-wt 1.0 0.0
<Figure size 216x216 with 0 Axes>
H3K9me3 H3K9me3 1.0 0.0
H3K9me3 H3K4me1 0.007933317702121464 0.7691523998107841
H3K9me3 H3K4me2 0.044228672221843755 0.10163817786462813
H3K9me3 H3K4me3 0.053737798650920314 0.04666004836886921
H3K9me3 H3K27ac 0.03718373706351468 0.16881454156123957
H3K9me3 H3K36me3 0.06906636672694122 0.010526278474959678
H3K9me3 H3K9ac 0.040046841077399725 0.13832510654544702
H3K9me3 mean_wt 0.04307736823791785 0.11086624765467276
H3K9me3 mean_ko 0.04734098647425971 0.07972581975723912
H3K9me3 mean_ko-wt 0.0348065516761931 0.1977468275954522
H3K9me3 H3K4me1 0.007933317702121464 0.7691523998107841
H3K9me3 H3K4me2 0.044228672221843755 0.10163817786462813
H3K9me3 H3K4me3 0.053737798650920314 0.04666004836886921
H3K9me3 H3K27ac 0.03718373706351468 0.16881454156123957
H3K9me3 H3K36me3 0.06906636672694122 0.010526278474959678
H3K9me3 H3K27me3 0.00869910621173833 0.7475934210362744
H3K9me3 Log2FC FB 0.012815616035329692 0.6354209842455663
H3K9me3 Log2FC MB -0.0103079338658799 0.7029553969364577
H3K9me3 Log2FC HB -0.00026066324238072645 0.9923062947000574
H3K9me3 Log2FC SC -0.0017808952958044651 0.9474724511139466
H3K9me3 VAE0 0.03537250227723582 0.19055019655164637
H3K9me3 VAE1 -0.011191335427176704 0.6788618286935884
H3K9me3 VAE2 -0.05527632501500181 0.04071553265967362
H3K4me1 H3K9me3 0.007933317702121464 0.7691523998107841
H3K4me1 H3K4me1 1.0 0.0
H3K4me1 H3K4me2 0.5778097092631771 6.30995115822728e-123
H3K4me1 H3K4me3 0.5424054244750087 1.0462500992593941e-105
H3K4me1 H3K27ac 0.4474397884575597 1.868776646979828e-68
H3K4me1 H3K36me3 0.23331708205528487 2.0919177335021665e-18
H3K4me1 H3K9ac 0.49904269037186083 3.110616458783235e-87
H3K4me1 mean_wt 0.24278998959573111 7.627059760649075e-20
H3K4me1 mean_ko 0.23956359735363475 2.3946366296042235e-19
H3K4me1 mean_ko-wt 0.025077000286833816 0.35349809962892376
H3K4me1 H3K4me1 1.0 0.0
H3K4me1 H3K4me2 0.5778097092631771 6.30995115822728e-123
H3K4me1 H3K4me3 0.5424054244750087 1.0462500992593941e-105
H3K4me1 H3K27ac 0.4474397884575597 1.868776646979828e-68
H3K4me1 H3K36me3 0.23331708205528487 2.0919177335021665e-18
H3K4me1 H3K27me3 0.4183583143974251 3.2720909943747853e-59
H3K4me1 Log2FC FB -0.005828660227440098 0.829281719129814
H3K4me1 Log2FC MB -0.07363958460652882 0.006374445946102697
H3K4me1 Log2FC HB -0.12856724647180287 1.7885836117251365e-06
H3K4me1 Log2FC SC 0.01896101107434295 0.48299579643865487
H3K4me1 VAE0 0.29031611063142665 4.930166724775107e-28
H3K4me1 VAE1 -0.4294316663802427 1.2627522245545507e-62
H3K4me1 VAE2 0.1608669464307792 2.097868335239766e-09
H3K4me2 H3K9me3 0.04422867222184375 0.1016381778646282
H3K4me2 H3K4me1 0.5778097092631771 6.30995115822728e-123
H3K4me2 H3K4me2 1.0 0.0
H3K4me2 H3K4me3 0.9249576243822666 0.0
H3K4me2 H3K27ac 0.5809337496355107 1.505979846743912e-124
H3K4me2 H3K36me3 0.2978871153402344 1.7085900450507975e-29
H3K4me2 H3K9ac 0.8627487422150418 0.0
H3K4me2 mean_wt 0.3900660554177981 4.727264630671039e-51
H3K4me2 mean_ko 0.35797046417790596 1.0292297432585097e-42
H3K4me2 mean_ko-wt -0.02879671157287191 0.28664758486556835
H3K4me2 H3K4me1 0.5778097092631771 6.30995115822728e-123
H3K4me2 H3K4me2 1.0 0.0
H3K4me2 H3K4me3 0.9249576243822666 0.0
H3K4me2 H3K27ac 0.5809337496355107 1.505979846743912e-124
H3K4me2 H3K36me3 0.2978871153402344 1.7085900450507975e-29
H3K4me2 H3K27me3 0.32314796735960055 1.0851872748676937e-34
H3K4me2 Log2FC FB -0.09022151932051348 0.0008245804102662129
H3K4me2 Log2FC MB -0.18060329215713622 1.6216971012303665e-11
H3K4me2 Log2FC HB -0.2789470609848839 6.36148954888253e-26
H3K4me2 Log2FC SC -0.08115312156234512 0.0026377199644407583
H3K4me2 VAE0 0.41307606418431353 1.255080109427997e-57
H3K4me2 VAE1 -0.3459967816690149 7.715291848745455e-40
H3K4me2 VAE2 0.0547856481985845 0.042537576997147844
H3K4me3 H3K9me3 0.053737798650920314 0.04666004836886921
H3K4me3 H3K4me1 0.5424054244750087 1.0462500992593941e-105
H3K4me3 H3K4me2 0.9249576243822665 0.0
H3K4me3 H3K4me3 1.0 0.0
H3K4me3 H3K27ac 0.6651601396750653 7.046953869800731e-176
H3K4me3 H3K36me3 0.38465069892378145 1.4060008082927108e-49
H3K4me3 H3K9ac 0.9030038842756943 0.0
H3K4me3 mean_wt 0.47415516734627033 8.943496244161752e-78
H3K4me3 mean_ko 0.4341695065699847 3.994694583459608e-64
H3K4me3 mean_ko-wt -0.0951739358222856 0.0004174400224976428
H3K4me3 H3K4me1 0.5424054244750087 1.0462500992593941e-105
H3K4me3 H3K4me2 0.9249576243822665 0.0
H3K4me3 H3K4me3 1.0 0.0
H3K4me3 H3K27ac 0.6651601396750653 7.046953869800731e-176
H3K4me3 H3K36me3 0.38465069892378145 1.4060008082927108e-49
H3K4me3 H3K27me3 0.28080152722934676 2.9236513393994367e-26
H3K4me3 Log2FC FB -0.1552552713147644 7.538405719288966e-09
H3K4me3 Log2FC MB -0.25008860580419645 5.3855237481581464e-21
H3K4me3 Log2FC HB -0.33927820537868725 2.7958816066164293e-38
H3K4me3 Log2FC SC -0.1462532917403755 5.338305069703614e-08
H3K4me3 VAE0 0.5049970332540887 1.2931183112619104e-89
H3K4me3 VAE1 -0.29554922126419003 4.8781949636658434e-29
H3K4me3 VAE2 0.0824669217392564 0.0022436715756004757
H3K27ac H3K9me3 0.03718373706351468 0.16881454156123957
H3K27ac H3K4me1 0.44743978845755966 1.868776646980095e-68
H3K27ac H3K4me2 0.5809337496355107 1.505979846743912e-124
H3K27ac H3K4me3 0.6651601396750653 7.046953869800731e-176
H3K27ac H3K27ac 1.0 0.0
H3K27ac H3K36me3 0.40924313296843956 1.699390424794447e-56
H3K27ac H3K9ac 0.7227237508904014 6.882193462599804e-222
H3K27ac mean_wt 0.529031154228644 1.0127317607575462e-99
H3K27ac mean_ko 0.46561586007210876 1.0496706851591415e-74
H3K27ac mean_ko-wt -0.10802112651026943 6.129116274337846e-05
H3K27ac H3K4me1 0.44743978845755966 1.868776646980095e-68
H3K27ac H3K4me2 0.5809337496355107 1.505979846743912e-124
H3K27ac H3K4me3 0.6651601396750653 7.046953869800731e-176
H3K27ac H3K27ac 1.0 0.0
H3K27ac H3K36me3 0.40924313296843956 1.699390424794447e-56
H3K27ac H3K27me3 0.1335242473543216 6.986885390846781e-07
H3K27ac Log2FC FB -0.1895952937422755 1.462114117086756e-12
H3K27ac Log2FC MB -0.27333105823559734 6.462122594893282e-25
H3K27ac Log2FC HB -0.34523770917786767 1.162608260691108e-39
H3K27ac Log2FC SC -0.19369439965896984 4.689838803083501e-13
H3K27ac VAE0 0.565460701897515 1.1067285645987936e-116
H3K27ac VAE1 -0.13974158340089593 2.0471583971385752e-07
H3K27ac VAE2 0.02811476348996577 0.29821822708243745
H3K36me3 H3K9me3 0.0690663667269412 0.010526278474959691
H3K36me3 H3K4me1 0.23331708205528484 2.0919177335021665e-18
H3K36me3 H3K4me2 0.2978871153402344 1.7085900450507975e-29
H3K36me3 H3K4me3 0.38465069892378145 1.4060008082927108e-49
H3K36me3 H3K27ac 0.40924313296843967 1.6993904247943983e-56
H3K36me3 H3K36me3 1.0 0.0
H3K36me3 H3K9ac 0.3916349805435466 1.7480657850104863e-51
H3K36me3 mean_wt 0.4861825101262901 3.0054313008082013e-82
H3K36me3 mean_ko 0.4231395387694597 1.1391534566952996e-60
H3K36me3 mean_ko-wt -0.15668201024884568 5.469081652632577e-09
H3K36me3 H3K4me1 0.23331708205528484 2.0919177335021665e-18
H3K36me3 H3K4me2 0.2978871153402344 1.7085900450507975e-29
H3K36me3 H3K4me3 0.38465069892378145 1.4060008082927108e-49
H3K36me3 H3K27ac 0.40924313296843967 1.6993904247943983e-56
H3K36me3 H3K36me3 1.0 0.0
H3K36me3 H3K27me3 -0.060811738887076326 0.024340755777373508
H3K36me3 Log2FC FB -0.22787654840262245 1.3131908798832538e-17
H3K36me3 Log2FC MB -0.29142012227610314 3.038589462812012e-28
H3K36me3 Log2FC HB -0.27428074788659457 4.382946347360937e-25
H3K36me3 Log2FC SC -0.23820189269891243 3.861529498749434e-19
H3K36me3 VAE0 0.5128226801227292 8.11907865775757e-93
H3K36me3 VAE1 0.04696623939013081 0.08214161188440461
H3K36me3 VAE2 0.016248697834837858 0.5477522547037476
H3K9ac H3K9me3 0.040046841077399725 0.13832510654544702
H3K9ac H3K4me1 0.49904269037186083 3.110616458783235e-87
H3K9ac H3K4me2 0.8627487422150417 0.0
H3K9ac H3K4me3 0.9030038842756942 0.0
H3K9ac H3K27ac 0.7227237508904014 6.882193462599804e-222
H3K9ac H3K36me3 0.3916349805435467 1.7480657850104365e-51
H3K9ac H3K9ac 0.9999999999999999 0.0
H3K9ac mean_wt 0.49764003406030627 1.1141987545204845e-86
H3K9ac mean_ko 0.44585615791285194 6.286794883249051e-68
H3K9ac mean_ko-wt -0.08196167319953047 0.002388351628186672
H3K9ac H3K4me1 0.49904269037186083 3.110616458783235e-87
H3K9ac H3K4me2 0.8627487422150417 0.0
H3K9ac H3K4me3 0.9030038842756942 0.0
H3K9ac H3K27ac 0.7227237508904014 6.882193462599804e-222
H3K9ac H3K36me3 0.3916349805435467 1.7480657850104365e-51
H3K9ac H3K27me3 0.2432541671522939 6.460542891372985e-20
H3K9ac Log2FC FB -0.14556050806029827 6.176589798387343e-08
H3K9ac Log2FC MB -0.26073799669245706 9.625297236678724e-23
H3K9ac Log2FC HB -0.37619702156583873 2.47285337398766e-47
H3K9ac Log2FC SC -0.15053741055323813 2.1334988932288076e-08
H3K9ac VAE0 0.5039806392576622 3.322369405336695e-89
H3K9ac VAE1 -0.25411824491596513 1.2007357717167866e-21
H3K9ac VAE2 -0.007141371329901024 0.7916367064605488
mean_wt H3K9me3 0.04307736823791785 0.11086624765467276
mean_wt H3K4me1 0.24278998959573111 7.627059760649075e-20
mean_wt H3K4me2 0.39006605541779815 4.7272646306710386e-51
mean_wt H3K4me3 0.4741551673462703 8.943496244162007e-78
mean_wt H3K27ac 0.529031154228644 1.0127317607575462e-99
mean_wt H3K36me3 0.4861825101262901 3.0054313008082013e-82
mean_wt H3K9ac 0.49764003406030627 1.1141987545204845e-86
mean_wt mean_wt 1.0 0.0
mean_wt mean_ko 0.9286957551860459 0.0
mean_wt mean_ko-wt 0.04740020285089276 0.07934941563683097
mean_wt H3K4me1 0.24278998959573111 7.627059760649075e-20
mean_wt H3K4me2 0.39006605541779815 4.7272646306710386e-51
mean_wt H3K4me3 0.4741551673462703 8.943496244162007e-78
mean_wt H3K27ac 0.529031154228644 1.0127317607575462e-99
mean_wt H3K36me3 0.4861825101262901 3.0054313008082013e-82
mean_wt H3K27me3 0.05283914375024466 0.050458340528738155
mean_wt Log2FC FB -0.2653405981699492 1.594623955555886e-23
mean_wt Log2FC MB -0.41511072779501845 3.104193312627587e-58
mean_wt Log2FC HB -0.4325699086750423 1.2898277385832e-63
mean_wt Log2FC SC -0.36336702772016066 4.740409315607139e-44
mean_wt VAE0 0.9225507496267616 0.0
mean_wt VAE1 -0.0843331888079318 0.0017760748791830353
mean_wt VAE2 -0.3780081450486794 8.274659191134132e-48
mean_ko H3K9me3 0.04734098647425971 0.07972581975723912
mean_ko H3K4me1 0.23956359735363478 2.3946366296042235e-19
mean_ko H3K4me2 0.357970464177906 1.0292297432585098e-42
mean_ko H3K4me3 0.43416950656998476 3.994694583459039e-64
mean_ko H3K27ac 0.4656158600721088 1.0496706851591115e-74
mean_ko H3K36me3 0.4231395387694597 1.1391534566952996e-60
mean_ko H3K9ac 0.44585615791285194 6.286794883249051e-68
mean_ko mean_wt 0.9286957551860459 0.0
mean_ko mean_ko 1.0 0.0
mean_ko mean_ko-wt 0.32128358579954336 2.732292140596804e-34
mean_ko H3K4me1 0.23956359735363478 2.3946366296042235e-19
mean_ko H3K4me2 0.357970464177906 1.0292297432585098e-42
mean_ko H3K4me3 0.43416950656998476 3.994694583459039e-64
mean_ko H3K27ac 0.4656158600721088 1.0496706851591115e-74
mean_ko H3K36me3 0.4231395387694597 1.1391534566952996e-60
mean_ko H3K27me3 0.1563236584109969 5.929773338450297e-09
mean_ko Log2FC FB -0.05017643771673508 0.06326006935890383
mean_ko Log2FC MB -0.18026114644845787 1.7729599808756164e-11
mean_ko Log2FC HB -0.21644940225034787 5.343808083414494e-16
mean_ko Log2FC SC -0.17550056368275996 6.022358017814123e-11
mean_ko VAE0 0.8281568932120574 0.0
mean_ko VAE1 -0.1975228461039166 1.5848762933272344e-13
mean_ko VAE2 -0.46508046817661947 1.6241298929221246e-74
mean_ko-wt H3K9me3 0.0348065516761931 0.1977468275954522
mean_ko-wt H3K4me1 0.025077000286833816 0.35349809962892376
mean_ko-wt H3K4me2 -0.02879671157287191 0.28664758486556835
mean_ko-wt H3K4me3 -0.0951739358222856 0.0004174400224976428
mean_ko-wt H3K27ac -0.10802112651026943 6.129116274337846e-05
mean_ko-wt H3K36me3 -0.15668201024884568 5.469081652632577e-09
mean_ko-wt H3K9ac -0.08196167319953047 0.002388351628186672
mean_ko-wt mean_wt 0.04740020285089276 0.07934941563683097
mean_ko-wt mean_ko 0.32128358579954336 2.732292140596804e-34
mean_ko-wt mean_ko-wt 1.0 0.0
mean_ko-wt H3K4me1 0.025077000286833816 0.35349809962892376
mean_ko-wt H3K4me2 -0.02879671157287191 0.28664758486556835
mean_ko-wt H3K4me3 -0.0951739358222856 0.0004174400224976428
mean_ko-wt H3K27ac -0.10802112651026943 6.129116274337846e-05
mean_ko-wt H3K36me3 -0.15668201024884568 5.469081652632577e-09
mean_ko-wt H3K27me3 0.3093270557262811 8.744563596689129e-32
mean_ko-wt Log2FC FB 0.5985413361401808 4.978980674984865e-134
mean_ko-wt Log2FC MB 0.6258503790520503 5.900203801100752e-150
mean_ko-wt Log2FC HB 0.5385254125390007 6.081246030219862e-104
mean_ko-wt Log2FC SC 0.5403812815085522 8.768998759461235e-105
mean_ko-wt VAE0 -0.012947891180987355 0.6319353745642039
mean_ko-wt VAE1 -0.3364180123508915 1.2551528216037472e-37
mean_ko-wt VAE2 -0.3786089351715797 5.746004360247138e-48
H3K4me1 H3K9me3 0.007933317702121464 0.7691523998107841
H3K4me1 H3K4me1 1.0 0.0
H3K4me1 H3K4me2 0.5778097092631771 6.30995115822728e-123
H3K4me1 H3K4me3 0.5424054244750087 1.0462500992593941e-105
H3K4me1 H3K27ac 0.4474397884575597 1.868776646979828e-68
H3K4me1 H3K36me3 0.23331708205528487 2.0919177335021665e-18
H3K4me1 H3K9ac 0.49904269037186083 3.110616458783235e-87
H3K4me1 mean_wt 0.24278998959573111 7.627059760649075e-20
H3K4me1 mean_ko 0.23956359735363475 2.3946366296042235e-19
H3K4me1 mean_ko-wt 0.025077000286833816 0.35349809962892376
H3K4me1 H3K4me1 1.0 0.0
H3K4me1 H3K4me2 0.5778097092631771 6.30995115822728e-123
H3K4me1 H3K4me3 0.5424054244750087 1.0462500992593941e-105
H3K4me1 H3K27ac 0.4474397884575597 1.868776646979828e-68
H3K4me1 H3K36me3 0.23331708205528487 2.0919177335021665e-18
H3K4me1 H3K27me3 0.4183583143974251 3.2720909943747853e-59
H3K4me1 Log2FC FB -0.005828660227440098 0.829281719129814
H3K4me1 Log2FC MB -0.07363958460652882 0.006374445946102697
H3K4me1 Log2FC HB -0.12856724647180287 1.7885836117251365e-06
H3K4me1 Log2FC SC 0.01896101107434295 0.48299579643865487
H3K4me1 VAE0 0.29031611063142665 4.930166724775107e-28
H3K4me1 VAE1 -0.4294316663802427 1.2627522245545507e-62
H3K4me1 VAE2 0.1608669464307792 2.097868335239766e-09
H3K4me2 H3K9me3 0.04422867222184375 0.1016381778646282
H3K4me2 H3K4me1 0.5778097092631771 6.30995115822728e-123
H3K4me2 H3K4me2 1.0 0.0
H3K4me2 H3K4me3 0.9249576243822666 0.0
H3K4me2 H3K27ac 0.5809337496355107 1.505979846743912e-124
H3K4me2 H3K36me3 0.2978871153402344 1.7085900450507975e-29
H3K4me2 H3K9ac 0.8627487422150418 0.0
H3K4me2 mean_wt 0.3900660554177981 4.727264630671039e-51
H3K4me2 mean_ko 0.35797046417790596 1.0292297432585097e-42
H3K4me2 mean_ko-wt -0.02879671157287191 0.28664758486556835
H3K4me2 H3K4me1 0.5778097092631771 6.30995115822728e-123
H3K4me2 H3K4me2 1.0 0.0
H3K4me2 H3K4me3 0.9249576243822666 0.0
H3K4me2 H3K27ac 0.5809337496355107 1.505979846743912e-124
H3K4me2 H3K36me3 0.2978871153402344 1.7085900450507975e-29
H3K4me2 H3K27me3 0.32314796735960055 1.0851872748676937e-34
H3K4me2 Log2FC FB -0.09022151932051348 0.0008245804102662129
H3K4me2 Log2FC MB -0.18060329215713622 1.6216971012303665e-11
H3K4me2 Log2FC HB -0.2789470609848839 6.36148954888253e-26
H3K4me2 Log2FC SC -0.08115312156234512 0.0026377199644407583
H3K4me2 VAE0 0.41307606418431353 1.255080109427997e-57
H3K4me2 VAE1 -0.3459967816690149 7.715291848745455e-40
H3K4me2 VAE2 0.0547856481985845 0.042537576997147844
H3K4me3 H3K9me3 0.053737798650920314 0.04666004836886921
H3K4me3 H3K4me1 0.5424054244750087 1.0462500992593941e-105
H3K4me3 H3K4me2 0.9249576243822665 0.0
H3K4me3 H3K4me3 1.0 0.0
H3K4me3 H3K27ac 0.6651601396750653 7.046953869800731e-176
H3K4me3 H3K36me3 0.38465069892378145 1.4060008082927108e-49
H3K4me3 H3K9ac 0.9030038842756943 0.0
H3K4me3 mean_wt 0.47415516734627033 8.943496244161752e-78
H3K4me3 mean_ko 0.4341695065699847 3.994694583459608e-64
H3K4me3 mean_ko-wt -0.0951739358222856 0.0004174400224976428
H3K4me3 H3K4me1 0.5424054244750087 1.0462500992593941e-105
H3K4me3 H3K4me2 0.9249576243822665 0.0
H3K4me3 H3K4me3 1.0 0.0
H3K4me3 H3K27ac 0.6651601396750653 7.046953869800731e-176
H3K4me3 H3K36me3 0.38465069892378145 1.4060008082927108e-49
H3K4me3 H3K27me3 0.28080152722934676 2.9236513393994367e-26
H3K4me3 Log2FC FB -0.1552552713147644 7.538405719288966e-09
H3K4me3 Log2FC MB -0.25008860580419645 5.3855237481581464e-21
H3K4me3 Log2FC HB -0.33927820537868725 2.7958816066164293e-38
H3K4me3 Log2FC SC -0.1462532917403755 5.338305069703614e-08
H3K4me3 VAE0 0.5049970332540887 1.2931183112619104e-89
H3K4me3 VAE1 -0.29554922126419003 4.8781949636658434e-29
H3K4me3 VAE2 0.0824669217392564 0.0022436715756004757
H3K27ac H3K9me3 0.03718373706351468 0.16881454156123957
H3K27ac H3K4me1 0.44743978845755966 1.868776646980095e-68
H3K27ac H3K4me2 0.5809337496355107 1.505979846743912e-124
H3K27ac H3K4me3 0.6651601396750653 7.046953869800731e-176
H3K27ac H3K27ac 1.0 0.0
H3K27ac H3K36me3 0.40924313296843956 1.699390424794447e-56
H3K27ac H3K9ac 0.7227237508904014 6.882193462599804e-222
H3K27ac mean_wt 0.529031154228644 1.0127317607575462e-99
H3K27ac mean_ko 0.46561586007210876 1.0496706851591415e-74
H3K27ac mean_ko-wt -0.10802112651026943 6.129116274337846e-05
H3K27ac H3K4me1 0.44743978845755966 1.868776646980095e-68
H3K27ac H3K4me2 0.5809337496355107 1.505979846743912e-124
H3K27ac H3K4me3 0.6651601396750653 7.046953869800731e-176
H3K27ac H3K27ac 1.0 0.0
H3K27ac H3K36me3 0.40924313296843956 1.699390424794447e-56
H3K27ac H3K27me3 0.1335242473543216 6.986885390846781e-07
H3K27ac Log2FC FB -0.1895952937422755 1.462114117086756e-12
H3K27ac Log2FC MB -0.27333105823559734 6.462122594893282e-25
H3K27ac Log2FC HB -0.34523770917786767 1.162608260691108e-39
H3K27ac Log2FC SC -0.19369439965896984 4.689838803083501e-13
H3K27ac VAE0 0.565460701897515 1.1067285645987936e-116
H3K27ac VAE1 -0.13974158340089593 2.0471583971385752e-07
H3K27ac VAE2 0.02811476348996577 0.29821822708243745
H3K36me3 H3K9me3 0.0690663667269412 0.010526278474959691
H3K36me3 H3K4me1 0.23331708205528484 2.0919177335021665e-18
H3K36me3 H3K4me2 0.2978871153402344 1.7085900450507975e-29
H3K36me3 H3K4me3 0.38465069892378145 1.4060008082927108e-49
H3K36me3 H3K27ac 0.40924313296843967 1.6993904247943983e-56
H3K36me3 H3K36me3 1.0 0.0
H3K36me3 H3K9ac 0.3916349805435466 1.7480657850104863e-51
H3K36me3 mean_wt 0.4861825101262901 3.0054313008082013e-82
H3K36me3 mean_ko 0.4231395387694597 1.1391534566952996e-60
H3K36me3 mean_ko-wt -0.15668201024884568 5.469081652632577e-09
H3K36me3 H3K4me1 0.23331708205528484 2.0919177335021665e-18
H3K36me3 H3K4me2 0.2978871153402344 1.7085900450507975e-29
H3K36me3 H3K4me3 0.38465069892378145 1.4060008082927108e-49
H3K36me3 H3K27ac 0.40924313296843967 1.6993904247943983e-56
H3K36me3 H3K36me3 1.0 0.0
H3K36me3 H3K27me3 -0.060811738887076326 0.024340755777373508
H3K36me3 Log2FC FB -0.22787654840262245 1.3131908798832538e-17
H3K36me3 Log2FC MB -0.29142012227610314 3.038589462812012e-28
H3K36me3 Log2FC HB -0.27428074788659457 4.382946347360937e-25
H3K36me3 Log2FC SC -0.23820189269891243 3.861529498749434e-19
H3K36me3 VAE0 0.5128226801227292 8.11907865775757e-93
H3K36me3 VAE1 0.04696623939013081 0.08214161188440461
H3K36me3 VAE2 0.016248697834837858 0.5477522547037476
H3K27me3 H3K9me3 0.00869910621173833 0.7475934210362744
H3K27me3 H3K4me1 0.4183583143974251 3.2720909943747853e-59
H3K27me3 H3K4me2 0.32314796735960055 1.0851872748676937e-34
H3K27me3 H3K4me3 0.28080152722934676 2.9236513393994367e-26
H3K27me3 H3K27ac 0.13352424735432158 6.986885390846871e-07
H3K27me3 H3K36me3 -0.060811738887076326 0.024340755777373508
H3K27me3 H3K9ac 0.2432541671522939 6.460542891372985e-20
H3K27me3 mean_wt 0.05283914375024467 0.050458340528738155
H3K27me3 mean_ko 0.1563236584109969 5.929773338450297e-09
H3K27me3 mean_ko-wt 0.3093270557262811 8.744563596689129e-32
H3K27me3 H3K4me1 0.4183583143974251 3.2720909943747853e-59
H3K27me3 H3K4me2 0.32314796735960055 1.0851872748676937e-34
H3K27me3 H3K4me3 0.28080152722934676 2.9236513393994367e-26
H3K27me3 H3K27ac 0.13352424735432158 6.986885390846871e-07
H3K27me3 H3K36me3 -0.060811738887076326 0.024340755777373508
H3K27me3 H3K27me3 1.0 0.0
H3K27me3 Log2FC FB 0.34922344775478703 1.3330712289599865e-40
H3K27me3 Log2FC MB 0.21994143742472344 1.7596945962900848e-16
H3K27me3 Log2FC HB 0.05577417477278321 0.03893482554600838
H3K27me3 Log2FC SC 0.2297418333448438 7.032519975432944e-18
H3K27me3 VAE0 0.03803275569023637 0.15928977898633345
H3K27me3 VAE1 -0.9347886653056977 0.0
H3K27me3 VAE2 0.0635811903302392 0.018549792310585875
Log2FC FB H3K9me3 0.012815616035329692 0.6354209842455663
Log2FC FB H3K4me1 -0.005828660227440098 0.829281719129814
Log2FC FB H3K4me2 -0.09022151932051346 0.0008245804102662144
Log2FC FB H3K4me3 -0.1552552713147644 7.538405719288966e-09
Log2FC FB H3K27ac -0.1895952937422755 1.462114117086756e-12
Log2FC FB H3K36me3 -0.22787654840262248 1.3131908798832538e-17
Log2FC FB H3K9ac -0.14556050806029827 6.176589798387343e-08
Log2FC FB mean_wt -0.2653405981699492 1.594623955555886e-23
Log2FC FB mean_ko -0.05017643771673508 0.06326006935890383
Log2FC FB mean_ko-wt 0.5985413361401808 4.978980674984865e-134
Log2FC FB H3K4me1 -0.005828660227440098 0.829281719129814
Log2FC FB H3K4me2 -0.09022151932051346 0.0008245804102662144
Log2FC FB H3K4me3 -0.1552552713147644 7.538405719288966e-09
Log2FC FB H3K27ac -0.1895952937422755 1.462114117086756e-12
Log2FC FB H3K36me3 -0.22787654840262248 1.3131908798832538e-17
Log2FC FB H3K27me3 0.349223447754787 1.3330712289599865e-40
Log2FC FB Log2FC FB 1.0 0.0
Log2FC FB Log2FC MB 0.4874154790987053 1.0212500251884752e-82
Log2FC FB Log2FC HB 0.2538593319769123 1.323350676401945e-21
Log2FC FB Log2FC SC 0.3847565054541962 1.316627432201345e-49
Log2FC FB VAE0 -0.35659342295581337 2.2360651454584055e-42
Log2FC FB VAE1 -0.42546598386238704 2.180071569852324e-61
Log2FC FB VAE2 -0.2673799783130145 7.108362262493554e-24
Log2FC MB H3K9me3 -0.0103079338658799 0.7029553969364577
Log2FC MB H3K4me1 -0.07363958460652881 0.0063744459461027095
Log2FC MB H3K4me2 -0.18060329215713622 1.6216971012303665e-11
Log2FC MB H3K4me3 -0.2500886058041965 5.385523748158109e-21
Log2FC MB H3K27ac -0.27333105823559734 6.462122594893282e-25
Log2FC MB H3K36me3 -0.29142012227610314 3.038589462812012e-28
Log2FC MB H3K9ac -0.26073799669245706 9.625297236678724e-23
Log2FC MB mean_wt -0.4151107277950184 3.1041933126277644e-58
Log2FC MB mean_ko -0.18026114644845787 1.7729599808756164e-11
Log2FC MB mean_ko-wt 0.6258503790520503 5.900203801100752e-150
Log2FC MB H3K4me1 -0.07363958460652881 0.0063744459461027095
Log2FC MB H3K4me2 -0.18060329215713622 1.6216971012303665e-11
Log2FC MB H3K4me3 -0.2500886058041965 5.385523748158109e-21
Log2FC MB H3K27ac -0.27333105823559734 6.462122594893282e-25
Log2FC MB H3K36me3 -0.29142012227610314 3.038589462812012e-28
Log2FC MB H3K27me3 0.2199414374247234 1.7596945962901607e-16
Log2FC MB Log2FC FB 0.48741547909870536 1.0212500251884752e-82
Log2FC MB Log2FC MB 1.0 0.0
Log2FC MB Log2FC HB 0.5825485565927759 2.1497777521472687e-125
Log2FC MB Log2FC SC 0.41836646559213686 3.253563739871815e-59
Log2FC MB VAE0 -0.4109226755999542 5.448112954456422e-57
Log2FC MB VAE1 -0.23624776872255865 7.626021772616763e-19
Log2FC MB VAE2 -0.02289057494832727 0.39704663424441755
Log2FC HB H3K9me3 -0.00026066324238072645 0.9923062947000574
Log2FC HB H3K4me1 -0.1285672464718029 1.788583611725127e-06
Log2FC HB H3K4me2 -0.278947060984884 6.361489548882348e-26
Log2FC HB H3K4me3 -0.33927820537868725 2.7958816066164293e-38
Log2FC HB H3K27ac -0.34523770917786767 1.162608260691108e-39
Log2FC HB H3K36me3 -0.27428074788659457 4.382946347360937e-25
Log2FC HB H3K9ac -0.37619702156583873 2.47285337398766e-47
Log2FC HB mean_wt -0.4325699086750423 1.2898277385832e-63
Log2FC HB mean_ko -0.21644940225034787 5.343808083414494e-16
Log2FC HB mean_ko-wt 0.5385254125390005 6.081246030220727e-104
Log2FC HB H3K4me1 -0.1285672464718029 1.788583611725127e-06
Log2FC HB H3K4me2 -0.278947060984884 6.361489548882348e-26
Log2FC HB H3K4me3 -0.33927820537868725 2.7958816066164293e-38
Log2FC HB H3K27ac -0.34523770917786767 1.162608260691108e-39
Log2FC HB H3K36me3 -0.27428074788659457 4.382946347360937e-25
Log2FC HB H3K27me3 0.05577417477278321 0.03893482554600838
Log2FC HB Log2FC FB 0.2538593319769123 1.323350676401945e-21
Log2FC HB Log2FC MB 0.5825485565927759 2.1497777521472687e-125
Log2FC HB Log2FC HB 1.0 0.0
Log2FC HB Log2FC SC 0.4916008496130506 2.532062750739362e-84
Log2FC HB VAE0 -0.3358407592675868 1.696244835667893e-37
Log2FC HB VAE1 0.000893725395473486 0.9736252988078797
Log2FC HB VAE2 0.16871385420091436 3.2493899832182813e-10
Log2FC SC H3K9me3 -0.0017808952958044651 0.9474724511139466
Log2FC SC H3K4me1 0.01896101107434295 0.48299579643865487
Log2FC SC H3K4me2 -0.08115312156234512 0.0026377199644407583
Log2FC SC H3K4me3 -0.1462532917403755 5.338305069703614e-08
Log2FC SC H3K27ac -0.19369439965896987 4.689838803083501e-13
Log2FC SC H3K36me3 -0.23820189269891243 3.861529498749434e-19
Log2FC SC H3K9ac -0.15053741055323813 2.1334988932288076e-08
Log2FC SC mean_wt -0.36336702772016066 4.740409315607139e-44
Log2FC SC mean_ko -0.17550056368275996 6.022358017814123e-11
Log2FC SC mean_ko-wt 0.5403812815085522 8.768998759461235e-105
Log2FC SC H3K4me1 0.01896101107434295 0.48299579643865487
Log2FC SC H3K4me2 -0.08115312156234512 0.0026377199644407583
Log2FC SC H3K4me3 -0.1462532917403755 5.338305069703614e-08
Log2FC SC H3K27ac -0.19369439965896987 4.689838803083501e-13
Log2FC SC H3K36me3 -0.23820189269891243 3.861529498749434e-19
Log2FC SC H3K27me3 0.2297418333448438 7.032519975432944e-18
Log2FC SC Log2FC FB 0.38475650545419626 1.3166274322012883e-49
Log2FC SC Log2FC MB 0.4183664655921368 3.253563739871815e-59
Log2FC SC Log2FC HB 0.4916008496130506 2.532062750739362e-84
Log2FC SC Log2FC SC 1.0 0.0
Log2FC SC VAE0 -0.30086852336773884 4.42036845192071e-30
Log2FC SC VAE1 -0.19659919987030303 2.0632386010018646e-13
Log2FC SC VAE2 0.0544089035764286 0.043982879996444985
VAE0 H3K9me3 0.03537250227723582 0.19055019655164637
VAE0 H3K4me1 0.29031611063142665 4.930166724775107e-28
VAE0 H3K4me2 0.4130760641843135 1.255080109427997e-57
VAE0 H3K4me3 0.5049970332540886 1.2931183112620578e-89
VAE0 H3K27ac 0.565460701897515 1.1067285645987936e-116
VAE0 H3K36me3 0.5128226801227292 8.11907865775757e-93
VAE0 H3K9ac 0.5039806392576622 3.322369405336695e-89
VAE0 mean_wt 0.9225507496267616 0.0
VAE0 mean_ko 0.8281568932120574 0.0
VAE0 mean_ko-wt -0.012947891180987355 0.6319353745642039
VAE0 H3K4me1 0.29031611063142665 4.930166724775107e-28
VAE0 H3K4me2 0.4130760641843135 1.255080109427997e-57
VAE0 H3K4me3 0.5049970332540886 1.2931183112620578e-89
VAE0 H3K27ac 0.565460701897515 1.1067285645987936e-116
VAE0 H3K36me3 0.5128226801227292 8.11907865775757e-93
VAE0 H3K27me3 0.03803275569023637 0.15928977898633345
VAE0 Log2FC FB -0.3565934229558133 2.2360651454584692e-42
VAE0 Log2FC MB -0.41092267559995416 5.448112954456422e-57
VAE0 Log2FC HB -0.33584075926758683 1.696244835667772e-37
VAE0 Log2FC SC -0.30086852336773884 4.42036845192071e-30
VAE0 VAE0 1.0 0.0
VAE0 VAE1 -0.044440792633740046 0.10000658140919086
VAE0 VAE2 -0.06607105313289889 0.014410819464438108
VAE1 H3K9me3 -0.011191335427176704 0.6788618286935884
VAE1 H3K4me1 -0.42943166638024277 1.2627522245545507e-62
VAE1 H3K4me2 -0.3459967816690149 7.715291848745455e-40
VAE1 H3K4me3 -0.29554922126419003 4.8781949636658434e-29
VAE1 H3K27ac -0.13974158340089593 2.0471583971385752e-07
VAE1 H3K36me3 0.0469662393901308 0.08214161188440466
VAE1 H3K9ac -0.25411824491596513 1.2007357717167866e-21
VAE1 mean_wt -0.0843331888079318 0.0017760748791830353
VAE1 mean_ko -0.1975228461039166 1.5848762933272344e-13
VAE1 mean_ko-wt -0.3364180123508915 1.2551528216037472e-37
VAE1 H3K4me1 -0.42943166638024277 1.2627522245545507e-62
VAE1 H3K4me2 -0.3459967816690149 7.715291848745455e-40
VAE1 H3K4me3 -0.29554922126419003 4.8781949636658434e-29
VAE1 H3K27ac -0.13974158340089593 2.0471583971385752e-07
VAE1 H3K36me3 0.0469662393901308 0.08214161188440466
VAE1 H3K27me3 -0.9347886653056977 0.0
VAE1 Log2FC FB -0.4254659838623871 2.180071569852324e-61
VAE1 Log2FC MB -0.23624776872255862 7.626021772616817e-19
VAE1 Log2FC HB 0.0008937253954734859 0.9736252988078797
VAE1 Log2FC SC -0.19659919987030305 2.0632386010018648e-13
VAE1 VAE0 -0.044440792633740046 0.10000658140919086
VAE1 VAE1 1.0 0.0
VAE1 VAE2 -0.010831726378816332 0.6886318434581902
VAE2 H3K9me3 -0.055276325015001807 0.04071553265967369
VAE2 H3K4me1 0.1608669464307792 2.097868335239766e-09
VAE2 H3K4me2 0.0547856481985845 0.042537576997147844
VAE2 H3K4me3 0.08246692173925642 0.0022436715756004757
VAE2 H3K27ac 0.02811476348996577 0.29821822708243745
VAE2 H3K36me3 0.016248697834837858 0.5477522547037476
VAE2 H3K9ac -0.0071413713299010246 0.7916367064605488
VAE2 mean_wt -0.3780081450486794 8.274659191134132e-48
VAE2 mean_ko -0.46508046817661947 1.6241298929221246e-74
VAE2 mean_ko-wt -0.3786089351715797 5.746004360247138e-48
VAE2 H3K4me1 0.1608669464307792 2.097868335239766e-09
VAE2 H3K4me2 0.0547856481985845 0.042537576997147844
VAE2 H3K4me3 0.08246692173925642 0.0022436715756004757
VAE2 H3K27ac 0.02811476348996577 0.29821822708243745
VAE2 H3K36me3 0.016248697834837858 0.5477522547037476
VAE2 H3K27me3 0.06358119033023919 0.01854979231058591
VAE2 Log2FC FB -0.2673799783130146 7.108362262493199e-24
VAE2 Log2FC MB -0.02289057494832727 0.39704663424441755
VAE2 Log2FC HB 0.16871385420091436 3.2493899832182813e-10
VAE2 Log2FC SC 0.0544089035764286 0.043982879996444985
VAE2 VAE0 -0.06607105313289889 0.014410819464438108
VAE2 VAE1 -0.010831726378816332 0.6886318434581902
VAE2 VAE2 1.0 0.0
<Figure size 216x216 with 0 Axes>
H3K4me1 H3K4me1 1.0 0.0
H3K4me1 H3K4me2 0.5778097092631771 6.30995115822728e-123
H3K4me1 H3K4me3 0.5424054244750087 1.0462500992593941e-105
H3K4me1 H3K27ac 0.4474397884575597 1.868776646979828e-68
H3K4me1 H3K36me3 0.23331708205528487 2.0919177335021665e-18
H3K4me1 H3K27me3 0.4183583143974251 3.2720909943747853e-59
H3K4me1 Log2FC FB -0.005828660227440098 0.829281719129814
H3K4me1 Log2FC MB -0.07363958460652882 0.006374445946102697
H3K4me1 Log2FC HB -0.12856724647180287 1.7885836117251365e-06
H3K4me1 Log2FC SC 0.01896101107434295 0.48299579643865487
H3K4me1 VAE0 0.29031611063142665 4.930166724775107e-28
H3K4me1 VAE1 -0.4294316663802427 1.2627522245545507e-62
H3K4me1 VAE2 0.1608669464307792 2.097868335239766e-09
H3K4me2 H3K4me1 0.5778097092631771 6.30995115822728e-123
H3K4me2 H3K4me2 1.0 0.0
H3K4me2 H3K4me3 0.9249576243822666 0.0
H3K4me2 H3K27ac 0.5809337496355107 1.505979846743912e-124
H3K4me2 H3K36me3 0.2978871153402344 1.7085900450507975e-29
H3K4me2 H3K27me3 0.32314796735960055 1.0851872748676937e-34
H3K4me2 Log2FC FB -0.09022151932051348 0.0008245804102662129
H3K4me2 Log2FC MB -0.18060329215713622 1.6216971012303665e-11
H3K4me2 Log2FC HB -0.2789470609848839 6.36148954888253e-26
H3K4me2 Log2FC SC -0.08115312156234512 0.0026377199644407583
H3K4me2 VAE0 0.41307606418431353 1.255080109427997e-57
H3K4me2 VAE1 -0.3459967816690149 7.715291848745455e-40
H3K4me2 VAE2 0.0547856481985845 0.042537576997147844
H3K4me3 H3K4me1 0.5424054244750087 1.0462500992593941e-105
H3K4me3 H3K4me2 0.9249576243822665 0.0
H3K4me3 H3K4me3 1.0 0.0
H3K4me3 H3K27ac 0.6651601396750653 7.046953869800731e-176
H3K4me3 H3K36me3 0.38465069892378145 1.4060008082927108e-49
H3K4me3 H3K27me3 0.28080152722934676 2.9236513393994367e-26
H3K4me3 Log2FC FB -0.1552552713147644 7.538405719288966e-09
H3K4me3 Log2FC MB -0.25008860580419645 5.3855237481581464e-21
H3K4me3 Log2FC HB -0.33927820537868725 2.7958816066164293e-38
H3K4me3 Log2FC SC -0.1462532917403755 5.338305069703614e-08
H3K4me3 VAE0 0.5049970332540887 1.2931183112619104e-89
H3K4me3 VAE1 -0.29554922126419003 4.8781949636658434e-29
H3K4me3 VAE2 0.0824669217392564 0.0022436715756004757
H3K27ac H3K4me1 0.44743978845755966 1.868776646980095e-68
H3K27ac H3K4me2 0.5809337496355107 1.505979846743912e-124
H3K27ac H3K4me3 0.6651601396750653 7.046953869800731e-176
H3K27ac H3K27ac 1.0 0.0
H3K27ac H3K36me3 0.40924313296843956 1.699390424794447e-56
H3K27ac H3K27me3 0.1335242473543216 6.986885390846781e-07
H3K27ac Log2FC FB -0.1895952937422755 1.462114117086756e-12
H3K27ac Log2FC MB -0.27333105823559734 6.462122594893282e-25
H3K27ac Log2FC HB -0.34523770917786767 1.162608260691108e-39
H3K27ac Log2FC SC -0.19369439965896984 4.689838803083501e-13
H3K27ac VAE0 0.565460701897515 1.1067285645987936e-116
H3K27ac VAE1 -0.13974158340089593 2.0471583971385752e-07
H3K27ac VAE2 0.02811476348996577 0.29821822708243745
H3K36me3 H3K4me1 0.23331708205528484 2.0919177335021665e-18
H3K36me3 H3K4me2 0.2978871153402344 1.7085900450507975e-29
H3K36me3 H3K4me3 0.38465069892378145 1.4060008082927108e-49
H3K36me3 H3K27ac 0.40924313296843967 1.6993904247943983e-56
H3K36me3 H3K36me3 1.0 0.0
H3K36me3 H3K27me3 -0.060811738887076326 0.024340755777373508
H3K36me3 Log2FC FB -0.22787654840262245 1.3131908798832538e-17
H3K36me3 Log2FC MB -0.29142012227610314 3.038589462812012e-28
H3K36me3 Log2FC HB -0.27428074788659457 4.382946347360937e-25
H3K36me3 Log2FC SC -0.23820189269891243 3.861529498749434e-19
H3K36me3 VAE0 0.5128226801227292 8.11907865775757e-93
H3K36me3 VAE1 0.04696623939013081 0.08214161188440461
H3K36me3 VAE2 0.016248697834837858 0.5477522547037476
H3K27me3 H3K4me1 0.4183583143974251 3.2720909943747853e-59
H3K27me3 H3K4me2 0.32314796735960055 1.0851872748676937e-34
H3K27me3 H3K4me3 0.28080152722934676 2.9236513393994367e-26
H3K27me3 H3K27ac 0.13352424735432158 6.986885390846871e-07
H3K27me3 H3K36me3 -0.060811738887076326 0.024340755777373508
H3K27me3 H3K27me3 1.0 0.0
H3K27me3 Log2FC FB 0.34922344775478703 1.3330712289599865e-40
H3K27me3 Log2FC MB 0.21994143742472344 1.7596945962900848e-16
H3K27me3 Log2FC HB 0.05577417477278321 0.03893482554600838
H3K27me3 Log2FC SC 0.2297418333448438 7.032519975432944e-18
H3K27me3 VAE0 0.03803275569023637 0.15928977898633345
H3K27me3 VAE1 -0.9347886653056977 0.0
H3K27me3 VAE2 0.0635811903302392 0.018549792310585875
Log2FC FB H3K4me1 -0.005828660227440098 0.829281719129814
Log2FC FB H3K4me2 -0.09022151932051346 0.0008245804102662144
Log2FC FB H3K4me3 -0.1552552713147644 7.538405719288966e-09
Log2FC FB H3K27ac -0.1895952937422755 1.462114117086756e-12
Log2FC FB H3K36me3 -0.22787654840262248 1.3131908798832538e-17
Log2FC FB H3K27me3 0.349223447754787 1.3330712289599865e-40
Log2FC FB Log2FC FB 1.0 0.0
Log2FC FB Log2FC MB 0.4874154790987053 1.0212500251884752e-82
Log2FC FB Log2FC HB 0.2538593319769123 1.323350676401945e-21
Log2FC FB Log2FC SC 0.3847565054541962 1.316627432201345e-49
Log2FC FB VAE0 -0.35659342295581337 2.2360651454584055e-42
Log2FC FB VAE1 -0.42546598386238704 2.180071569852324e-61
Log2FC FB VAE2 -0.2673799783130145 7.108362262493554e-24
Log2FC MB H3K4me1 -0.07363958460652881 0.0063744459461027095
Log2FC MB H3K4me2 -0.18060329215713622 1.6216971012303665e-11
Log2FC MB H3K4me3 -0.2500886058041965 5.385523748158109e-21
Log2FC MB H3K27ac -0.27333105823559734 6.462122594893282e-25
Log2FC MB H3K36me3 -0.29142012227610314 3.038589462812012e-28
Log2FC MB H3K27me3 0.2199414374247234 1.7596945962901607e-16
Log2FC MB Log2FC FB 0.48741547909870536 1.0212500251884752e-82
Log2FC MB Log2FC MB 1.0 0.0
Log2FC MB Log2FC HB 0.5825485565927759 2.1497777521472687e-125
Log2FC MB Log2FC SC 0.41836646559213686 3.253563739871815e-59
Log2FC MB VAE0 -0.4109226755999542 5.448112954456422e-57
Log2FC MB VAE1 -0.23624776872255865 7.626021772616763e-19
Log2FC MB VAE2 -0.02289057494832727 0.39704663424441755
Log2FC HB H3K4me1 -0.1285672464718029 1.788583611725127e-06
Log2FC HB H3K4me2 -0.278947060984884 6.361489548882348e-26
Log2FC HB H3K4me3 -0.33927820537868725 2.7958816066164293e-38
Log2FC HB H3K27ac -0.34523770917786767 1.162608260691108e-39
Log2FC HB H3K36me3 -0.27428074788659457 4.382946347360937e-25
Log2FC HB H3K27me3 0.05577417477278321 0.03893482554600838
Log2FC HB Log2FC FB 0.2538593319769123 1.323350676401945e-21
Log2FC HB Log2FC MB 0.5825485565927759 2.1497777521472687e-125
Log2FC HB Log2FC HB 1.0 0.0
Log2FC HB Log2FC SC 0.4916008496130506 2.532062750739362e-84
Log2FC HB VAE0 -0.3358407592675868 1.696244835667893e-37
Log2FC HB VAE1 0.000893725395473486 0.9736252988078797
Log2FC HB VAE2 0.16871385420091436 3.2493899832182813e-10
Log2FC SC H3K4me1 0.01896101107434295 0.48299579643865487
Log2FC SC H3K4me2 -0.08115312156234512 0.0026377199644407583
Log2FC SC H3K4me3 -0.1462532917403755 5.338305069703614e-08
Log2FC SC H3K27ac -0.19369439965896987 4.689838803083501e-13
Log2FC SC H3K36me3 -0.23820189269891243 3.861529498749434e-19
Log2FC SC H3K27me3 0.2297418333448438 7.032519975432944e-18
Log2FC SC Log2FC FB 0.38475650545419626 1.3166274322012883e-49
Log2FC SC Log2FC MB 0.4183664655921368 3.253563739871815e-59
Log2FC SC Log2FC HB 0.4916008496130506 2.532062750739362e-84
Log2FC SC Log2FC SC 1.0 0.0
Log2FC SC VAE0 -0.30086852336773884 4.42036845192071e-30
Log2FC SC VAE1 -0.19659919987030303 2.0632386010018646e-13
Log2FC SC VAE2 0.0544089035764286 0.043982879996444985
VAE0 H3K4me1 0.29031611063142665 4.930166724775107e-28
VAE0 H3K4me2 0.4130760641843135 1.255080109427997e-57
VAE0 H3K4me3 0.5049970332540886 1.2931183112620578e-89
VAE0 H3K27ac 0.565460701897515 1.1067285645987936e-116
VAE0 H3K36me3 0.5128226801227292 8.11907865775757e-93
VAE0 H3K27me3 0.03803275569023637 0.15928977898633345
VAE0 Log2FC FB -0.3565934229558133 2.2360651454584692e-42
VAE0 Log2FC MB -0.41092267559995416 5.448112954456422e-57
VAE0 Log2FC HB -0.33584075926758683 1.696244835667772e-37
VAE0 Log2FC SC -0.30086852336773884 4.42036845192071e-30
VAE0 VAE0 1.0 0.0
VAE0 VAE1 -0.044440792633740046 0.10000658140919086
VAE0 VAE2 -0.06607105313289889 0.014410819464438108
VAE1 H3K4me1 -0.42943166638024277 1.2627522245545507e-62
VAE1 H3K4me2 -0.3459967816690149 7.715291848745455e-40
VAE1 H3K4me3 -0.29554922126419003 4.8781949636658434e-29
VAE1 H3K27ac -0.13974158340089593 2.0471583971385752e-07
VAE1 H3K36me3 0.0469662393901308 0.08214161188440466
VAE1 H3K27me3 -0.9347886653056977 0.0
VAE1 Log2FC FB -0.4254659838623871 2.180071569852324e-61
VAE1 Log2FC MB -0.23624776872255862 7.626021772616817e-19
VAE1 Log2FC HB 0.0008937253954734859 0.9736252988078797
VAE1 Log2FC SC -0.19659919987030305 2.0632386010018648e-13
VAE1 VAE0 -0.044440792633740046 0.10000658140919086
VAE1 VAE1 1.0 0.0
VAE1 VAE2 -0.010831726378816332 0.6886318434581902
VAE2 H3K4me1 0.1608669464307792 2.097868335239766e-09
VAE2 H3K4me2 0.0547856481985845 0.042537576997147844
VAE2 H3K4me3 0.08246692173925642 0.0022436715756004757
VAE2 H3K27ac 0.02811476348996577 0.29821822708243745
VAE2 H3K36me3 0.016248697834837858 0.5477522547037476
VAE2 H3K27me3 0.06358119033023919 0.01854979231058591
VAE2 Log2FC FB -0.2673799783130146 7.108362262493199e-24
VAE2 Log2FC MB -0.02289057494832727 0.39704663424441755
VAE2 Log2FC HB 0.16871385420091436 3.2493899832182813e-10
VAE2 Log2FC SC 0.0544089035764286 0.043982879996444985
VAE2 VAE0 -0.06607105313289889 0.014410819464438108
VAE2 VAE1 -0.010831726378816332 0.6886318434581902
VAE2 VAE2 1.0 0.0
<Figure size 216x216 with 0 Axes>
  external_gene_name  wt11fb_merged-rep  wt13fb_merged-rep  wt15fb_merged-rep  \
0              Foxg1           8.161763           9.036692           9.295708   
1                En2           0.494674           0.168652           0.353709   
2               Sox3           7.329884           7.220449           5.700304   
3              Hoxc9           0.070489           0.020139           0.114239   

   wt18fb_merged-rep  wt11mb_merged-rep  wt13mb_merged-rep  wt15mb_merged-rep  \
0           9.176002           3.555796           0.152191           0.326289   
1           0.413756           7.499362           8.031306           7.013888   
2           4.480171           7.569871           6.390573           4.469231   
3           0.111092           0.071978           0.022155           0.032423   

   wt18mb_merged-rep  wt11hb_merged-rep  ...  ko15mb_merged-rep  \
0           1.557369           2.814731  ...           4.962096   
1           6.668011           6.872836  ...           6.280874   
2           4.535570           7.260694  ...           3.079978   
3           0.016071           0.118288  ...           4.920412   

   ko18mb_merged-rep  ko11hb_merged-rep  ko13hb_merged-rep  ko15hb_merged-rep  \
0           5.039551           5.698836           4.590381           4.688194   
1           6.268489           4.803762           5.565673           6.119783   
2           3.464150           7.339934           3.056591           2.845606   
3           4.175629           2.662632           5.024299           4.750092   

   ko18hb_merged-rep  ko11sc_merged-rep  ko13sc_merged-rep  ko15sc_merged-rep  \
0           4.667710           1.161890           3.216371           3.642766   
1           4.860775           3.372272           5.201905           4.665178   
2           2.955592           6.693111           3.609604           3.244365   
3           4.168916           6.278640           5.842700           5.185208   

   ko18sc_merged-rep  
0           3.737086  
1           4.312455  
2           3.485175  
3           5.005286  

[4 rows x 33 columns]
  external_gene_name   wt11fb1   wt11fb2   wt13fb1   wt13fb2   wt15fb1  \
0              Foxg1  0.861245  0.812161  0.864059  0.876246  0.787098   
1                En2  0.406390  0.470401  0.317830  0.349386  0.219831   
2               Sox3  0.835937  0.853705  0.802427  0.792347  0.666717   
3              Hoxc9  0.000748  0.001360  0.000415  0.000424  0.000117   

    wt15fb2   wt18fb1   wt18fb2   wt11mb1  ...  \
0  0.760034  0.684773  0.764342  0.626801  ...   
1  0.218171  0.181988  0.216225  0.528045  ...   
2  0.622941  0.482761  0.580437  0.777947  ...   
3  0.000351  0.000109  0.000130  0.012668  ...   

   hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal_log2  \
0                                           0.884408                         
1                                           0.865631                         
2                                           0.197014                         
3                                           0.975048                         

   hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal_log2  \
0                                           0.929549                         
1                                           0.877201                         
2                                           0.255986                         
3                                           1.000000                         

   hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal_log2  \
0                                           0.800277                         
1                                           0.843778                         
2                                           0.027715                         
3                                           0.886447                         

   midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal_log2  \
0                                           0.635946                        
1                                           0.839909                        
2                                           0.314673                        
3                                           1.000000                        

   midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal_log2  \
0                                           0.657623                        
1                                           0.804888                        
2                                           0.101360                        
3                                           0.989989                        

   midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal_log2  \
0                                           0.675749                        
1                                           0.835075                        
2                                           0.357026                        
3                                           0.966018                        

   midbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF276UEL_signal_log2  \
0                                           0.704756                        
1                                           0.840373                        
2                                           0.323529                        
3                                           0.990771                        

   midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal_log2  \
0                                           0.853614                        
1                                           0.864859                        
2                                           0.340934                        
3                                           0.988904                        

   midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal_log2  \
0                                           0.742814                        
1                                           0.804218                        
2                                           0.315893                        
3                                           0.971802                        

   midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal_log2  
0                                           0.794061                       
1                                           0.863890                       
2                                           0.082138                       
3                                           0.958068                       

[4 rows x 98 columns]
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/matrix.py:1205: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. 
  self.fig.tight_layout(**tight_params)
  external_gene_name      VAE0      VAE1      VAE2
0              Foxg1  1.439024 -0.253037  2.145944
1                En2  0.859806 -1.007098  0.235467
2               Sox3  1.946739  0.479508  0.929930
3              Hoxc9 -1.507013 -3.432837 -1.692456

Visualisations

In [11]:
ko_cols = [c for c in df_input.columns if 'ko' in c]
wt_cols = [c for c in df_input.columns if 'wt' in c]
# get IDs of the KO cols 
ko_col_ids = []
for i, c in enumerate(df_input.columns):
    if 'ko' in c:
        ko_col_ids.append(i)


# Plot the input space WT and KO
merged_wt = [c for c in merged_cols if 'wt' in c]
merged_ko = [c for c in merged_cols if 'ko' in c]
mb_genes = ['En1', 'En2', 'Lmx1a', 'Otx2', 'Sall4']

plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes, 
                  merged_wt, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=True, cmap=bin_cmap,
                  method='input', title=f'All markers input WT {experiment_name}')

plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes, 
                  merged_ko, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=True,  cmap=bin_cmap,
                  method='input', title=f'All markers input KO {experiment_name}')

# Plot the decoding space for KO and then WT
plot_gene_heatmap(df_input, fb_genes, mb_genes, hb_genes, sc_genes, 
                  wt_cols, all_vae_data, vae_mse, 
                  gene_name, merge_reps=True, mark='H3K27me3', cmap=bin_cmap,
                  method='decoding', title=f'All markers decoding WT {experiment_name}')


# Plot the latent space
plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes, 
                  ['VAE0', 'VAE1', 'VAE2'], vae_data, vae_mse,
                  gene_name, 
                   cmap=bin_cmap,
                  mark='H3K27me3', method='latent', title=f'All markers latent {experiment_name}')


progenitors = ['Sox2', 'Sox1', 'Sox3', 'Hes1', 'Hes5']
neurons = ['Snap25', 'Syt1', 'Slc32a1','Slc17a6', 'Syn1']
glia = ['Cspg4', 'Aqp4', 'Slc6a11', 'Olig1', 'Igfbp3']


# Plot the input space
plot_gene_heatmap(df, progenitors, neurons, glia, [], 
                  merged_wt, vae_data, vae_mse,
                  gene_name, mark='H3K27me3', method='input', cmap=bin_cmap,
                  merge_reps=True, title=f'Time markers input WT {experiment_name}')
plot_gene_heatmap(df, progenitors, neurons, glia, [], 
                  merged_ko, vae_data, vae_mse,
                  gene_name, mark='H3K27me3', method='input', cmap=bin_cmap,
                  merge_reps=True, title=f'Time markers input KO {experiment_name}')
# Plot the decoding space
plot_gene_heatmap(df_input, progenitors, neurons, glia, [], 
                  wt_cols, vae_data, vae_mse, 
                  gene_name, mark='H3K27me3', method='decoding', cmap=bin_cmap,
                  merge_reps=True, title=f'Time markers decoding WT {experiment_name}')
# plot_gene_heatmap(df_input, progenitors, neurons, glia, [], 
#                   ko_cols, vae_data, vae_mse, 
#                   gene_name, mark='H3K27me3', method='decoding', cmap=bin_cmap,
#                   merge_reps=True, title=f'Time markers decoding KO {experiment_name}', 
#                   input_col_ids=ko_col_ids)

# Plot the latent space

# plot_gene_heatmap(df, progenitors, neurons, glia, [], 
#                   ['VAE0', 'VAE1', 'VAE2'], vae_data, vae_mse,
#                   gene_name, mark='H3K27me3', method='latent', 
#                    cmap=div_cmap,  title=f'Time markers latent {experiment_name}')
                  
# -----------------------------------------------------------------------------------
#                         Plot the genes on a scatter plot
# -----------------------------------------------------------------------------------
gene_markers_ns = [['Slc32a1', 'Gad1', 'Gad2', 'Slc6a1'],
                    ['Slc17a6', 'VGLUT2', 'Slc17a7', 'VGLUT1', 'Slc17a8', 'VGLUT3', 'Slc1a1', 'Slc1a2', 'Slc1a6'],
                    ['Chat', 'Slc5a7', 'Slc18a3', 'Ache'],
                    ['Tubb3', 'Snap25', 'Syt1'],
                    ['Gfap', 'Olig2'],
                    ['Sox2', 'Hes1', 'Hes5', 'Vim']]

marker_labels_ns = ['NS GABAergic', 'NS Glutamatergic', 'NS Cholinergic', 'NS Neurons', 'NS Glia', 'NS Progenitors']

vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_ns,
                            gene_markers_ns, output_dir=fig_dir, plt_bg=False,vae_data=vae_data,
                            title=f'Markers NS {experiment_name}', fig_type="pdf",
                            show_plt=True, save_fig=True)

gene_markers_sep = [['Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6'], 
                    ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4'], 
                    ['Hoxb1', 'Krox20', 'Fev', 'Hoxd3', 'Phox2b'],
                    ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11', 
                    'Hoxa13', 'Hoxb9', 'Hoxb13',  'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13'],
                    ['Ccna1', 'Ccna2', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Cdc25a', 
                    'Cdc25b', 'Cdc25c', 'E2f1', 'E2f2', 'E2f3', 'Mcm10', 'Mcm5', 'Mcm3', 'Mcm2', 'Cip2a'],
                    ['Cdkn1a', 'Cdkn1b', 'Cdkn1c', 'Cdkn2a', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d'],
                    ['Sox2', 'Sox1', 'Sox3', 'Hes1', 'Hes5'],
                    ['Snap25', 'Syt1', 'Slc32a1','Slc17a6', 'Syn1'],
                    ['Cspg4', 'Aqp4', 'Slc6a11', 'Olig1', 'Igfbp3'],
                    ['Foxg1'], 
                    ['En2'], 
                    ['Phox2b'],
                    ['Hoxc9'],
                    ['Sox3']
                    ]

marker_labels_sep = ['forebrain', 'midbrain', 'hindbrain',  'spinalcord', 
                     'Proliferation', 'Negative regulators of Cell Cycle', 
                    'progenitors', 'neurons', 'glia',
                     'Foxg1', 'En2', 'Phox2b', 'Hoxc9', 'Sox3'
                    ]
color_map = {}
i = 4
for c in marker_labels_sep:
    if 'brain' in c or 'spinal' in c:
        if 'spinal' in c:
            color_map[c] = sc_colour
        else:
            color_map[c] = get_tissue_colour(c.lower())
    else:
        color_map[c] = sci_colour[i]
    i += 1
vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
                            gene_markers_sep, output_dir=fig_dir, plt_bg=False,vae_data=vae_data, fig_type="svg",
                            title=f'Markers significant {experiment_name}',
                            show_plt=False, save_fig=False, color_map=color_map, angle_plot=15)

col_id = len(wt_cols)
ko_cols_i = [i for i, c in enumerate(df_input.columns) if 'ko' in c and 'merged' not in c]


plot_gene_heatmap(df_input, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'], 
                  df_input.columns, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False,  cmap=rna_cmap, input_col_ids=ko_cols_i,
                  method='decoding', title=f'RNA KO DEC {experiment_name}')
col_id = 0
wt_cols_i = [i for i, c in enumerate(df_input.columns) if 'wt' in c and 'merged' not in c]

plot_gene_heatmap(df_input, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'], 
                  df_input.columns, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False,  cmap=rna_cmap, input_col_ids=wt_cols_i,
                  method='decoding', title=f'RNA WT DEC {experiment_name}')
log2_cols = [c for c in df_input.columns if 'log2Fold' in c]
log2_cols_i = [i for i, c in enumerate(df_input.columns) if 'log2Fold' in c]

plot_gene_heatmap(df_input, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'], 
                  df_input.columns, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False,  cmap=div_cmap, input_col_ids=log2_cols_i,
                  method='decoding', title=f'log2FC DEC {experiment_name}')

h3k_cols = [c for c in df_input.columns if 'H3K27me3' in c and 'signal' in c and 'median' not in c and 'brain' in c]
h3k_cols_i = [i for i, c in enumerate(df_input.columns) if 'H3K27me3' in c and 'signal' in c and 'median' not in c and 'brain' in c]

plot_gene_heatmap(df_input, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'], 
                  df_input.columns, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False,  cmap=hist_cmap, input_col_ids=h3k_cols_i,
                  method='decoding', title=f'H3K WT DEC {experiment_name}')

# Ploot all the raw H3K values on teh scatter plot
h3k_cols = [c for c in df.columns if 'H3K27me3' in c and 'signal' in c and 'median' not in c and 'brain' in c]
tmp_df = df.fillna(0)
feature_obs_ax = vae_vis.plot_feature_scatters(tmp_df, "", columns=h3k_cols, vae_data=vae_data, 
                                               show_plt=False, output_dir=fig_dir, fig_type="svg", 
                                               save_fig=False, title=f'{experiment_name}',
                                               angle_plot=315, cmap=hist_cmap, vmin=0, vmax=10)


# Plot  genes on heatmap
plot_gene_heatmap(df, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'], 
                  ko_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=True,  cmap=rna_cmap,
                  method='input', title=f'RNA KO {experiment_name}')
plot_gene_heatmap(df, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'], 
                  wt_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=True,  cmap=rna_cmap,
                  method='input', title=f'RNA WT {experiment_name}')
h3k_cols = [c for c in df.columns if 'H3K27me3' in c and 'signal' in c and 'median' not in c and 'brain' in c]
print(h3k_cols)
plot_gene_heatmap(df, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'], 
                  h3k_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=True,  cmap=hist_cmap,
                  method='input', title=f'H3K WT {experiment_name}')
log2_cols = [c for c in df.columns if 'log2Fold' in c]
print(log2_cols)
plot_gene_heatmap(df, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'], 
                  log2_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=True,  cmap=div_cmap,
                  method='input', title=f'log2FC {experiment_name}')

ko_cols = [c for c in df_all if 'ko' in c and 'merged' in c]
plot_gene_heatmap(df_all, ['Sox1'], ['Sox2'], ['Sox3'], [], 
                  ko_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=True,  cmap=rna_cmap,
                  method='input', title=f'Sox RNA KO {experiment_name}')
wt_cols = [c for c in df_all if 'wt' in c and 'merged' in c]

plot_gene_heatmap(df_all, ['Sox1'], ['Sox2'], ['Sox3'], [],
                  wt_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=True,  cmap=rna_cmap,
                  method='input', title=f'Sox RNA WT {experiment_name}')

plot_gene_heatmap(df, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'], 
                  ['VAE0', 'VAE1', 'VAE2'], all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False,  cmap=bin_cmap,
                  method='input', title=f'Latent {experiment_name}')

# Plot feature correlation
vae_vis.plot_feature_correlation(df_observed, '', columns=observed_cols, show_plt=True, cmap="RdBu_r",
                                 title=f'Feature vs observed features {experiment_name}',
                             output_dir=fig_dir, save_fig=True)

# Plot feature correlations for heatmap 
vae_vis.plot_feature_correlation(df, '', columns=vae_cols, show_plt=True, cmap='RdBu_r',
                             output_dir=fig_dir, save_fig=True, title=f'features VAE {experiment_name}')


"""
Plot the heatmaps of the Hox and other tisue specific genes
"""
plot_gene_heatmap(df_all, ['Pax2'], ['Hoxb9'], ['Hoxc9'], ['Hoxd9'],
                  wt_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False,  cmap=rna_cmap,
                  method='input', title=f'Sox pax2 RNA WT {experiment_name}')

plot_gene_heatmap(df_all, ['Pax2'], ['Hoxb9'], ['Hoxc9'], ['Hoxd9'],
                  ko_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False,  cmap=rna_cmap,
                  method='input', title=f'pax2 RNA KO {experiment_name}')

mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4']
hb_genes = ['Phox2b', 'Krox20', 'Fev', 'Hoxb1',  'Hoxd3']
mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4', 'Pax7', 'Lhx9', 'Evx1', 'Sox14', 
           'Gata3', 'Dmbx1', 'Tal1', 'Pou4f3', 'Pouf4f2', 'Gata3', 'Barhl1', 'Pax5', 'Dmbx1',
           'Pou4f1', 'Irx4', 'Pax7', 'Pax1', 'Pax8', 'Ebf2', 'Patx3', 'Onecut1', 'Otx2', 
           'Lhx5', 'Irx3', 'Irx5', 'Ebf3', 'Foxb1', 'Irx2', 'Shox2', 'Foxa2', 'Irx1', 'Tcfap2b']

plot_gene_heatmap(df_all, ['Otx2'], ['En2'], ['Phox2b'], ['Hoxd3'],
                  wt_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False,  cmap=rna_cmap,
                  method='input', title=f'Mid RNA WT {experiment_name}')


plot_gene_heatmap(df_all, ['Otx2'], ['En2'], ['Phox2b'], ['Hoxd3'],
                  ko_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False,  cmap=rna_cmap,
                  method='input', title=f'Mid RNA KO {experiment_name}')


# Plot gene names on scatter
gene_markers_sep = [['Cdkn1a'], ['Cdkn2a'], ['Cdkn2b'], ['Cip2a'], ['Cdc25c'], ['Mcm10'],
                    ['E2f1'], ['Ccna2'], ['Ccnd1'], ['Aqp4'], ['Snap25'], ['Hes5']
                    ]

marker_labels_sep = ['Cdkn1a', 'Cdkn2a', 'Cdkn2b',  'Cip2a', 
                     'Cdc25c', 'Mcm10', 
                    'E2f1', 'Ccna2', 'Ccnd1',
                     'Aqp4', 'Snap25', 'Hes5'
                    ]
color_map = {}
i = 4
for c in marker_labels_sep:
    if 'brain' in c or 'spinal' in c:
        if 'spinal' in c:
            color_map[c] = sc_colour
        else:
            color_map[c] = get_tissue_colour(c.lower())
    else:
        color_map[c] = sci_colour[i]
    i += 1
vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
                            gene_markers_sep, output_dir=fig_dir, plt_bg=False,vae_data=vae_data, fig_type="svg",
                            title=f'Markers specific {experiment_name}',
                            show_plt=False, save_fig=False, color_map=color_map, angle_plot=315)
  external_gene_name  wt11fb_merged-rep  wt13fb_merged-rep  wt15fb_merged-rep  \
0              Foxg1           0.876270           0.971738           1.000000   
1               Lhx6           0.523575           0.918995           0.942159   
2               Tbr1           0.699981           0.964443           1.000000   
3               Emx1           0.835460           1.000000           0.930334   
4              Eomes           0.846631           1.000000           0.988013   

   wt18fb_merged-rep  wt11mb_merged-rep  wt13mb_merged-rep  wt15mb_merged-rep  \
0           0.986938           0.373690           0.002306           0.021302   
1           1.000000           0.307570           0.193575           0.177374   
2           0.988598           0.398743           0.167463           0.246851   
3           0.803598           0.396227           0.223458           0.136889   
4           0.851878           0.517927           0.330729           0.369411   

   wt18mb_merged-rep  wt11hb_merged-rep  wt13hb_merged-rep  wt15hb_merged-rep  \
0           0.155632           0.292829           0.074193           0.048903   
1           0.327673           0.309588           0.000000           0.053135   
2           0.276605           0.007015           0.015224           0.022867   
3           0.070040           0.036411           0.001018           0.002749   
4           0.565673           0.038614           0.049856           0.060991   

   wt18hb_merged-rep  wt11sc_merged-rep  wt13sc_merged-rep  wt15sc_merged-rep  \
0           0.043263           0.050391           0.007295           0.000000   
1           0.173792           0.260517           0.233555           0.259264   
2           0.065809           0.000000           0.015840           0.046058   
3           0.005990           0.029781           0.007285           0.000000   
4           0.387983           0.002949           0.019690           0.027024   

   wt18sc_merged-rep  
0           0.010509  
1           0.239460  
2           0.020940  
3           0.003238  
4           0.000000  
  external_gene_name  ko11fb_merged-rep  ko13fb_merged-rep  ko15fb_merged-rep  \
0              Foxg1           0.773348           1.000000           0.887473   
1               Lhx6           0.398225           0.687109           0.758063   
2               Tbr1           0.403798           1.000000           0.914116   
3               Emx1           0.741702           1.000000           0.729911   
4              Eomes           0.672796           1.000000           0.738654   

   ko18fb_merged-rep  ko11mb_merged-rep  ko13mb_merged-rep  ko15mb_merged-rep  \
0           0.849753           0.331556           0.553470           0.618689   
1           1.000000           0.179405           0.133045           0.535425   
2           0.857413           0.031918           0.452307           0.592030   
3           0.616446           0.185984           0.270611           0.277571   
4           0.527092           0.124368           0.442612           0.448207   

   ko18mb_merged-rep  ko11hb_merged-rep  ko13hb_merged-rep  ko15hb_merged-rep  \
0           0.631299           0.738634           0.558173           0.574097   
1           0.910984           0.461606           0.381082           0.704271   
2           0.580405           0.415415           0.470464           0.569589   
3           0.247316           0.656964           0.096604           0.200821   
4           0.264649           0.663738           0.160908           0.311206   

   ko18hb_merged-rep  ko11sc_merged-rep  ko13sc_merged-rep  ko15sc_merged-rep  \
0           0.570762                0.0           0.334478           0.403897   
1           0.941213                0.0           0.234253           0.533167   
2           0.549105                0.0           0.343418           0.473450   
3           0.225002                0.0           0.036088           0.176869   
4           0.038210                0.0           0.142803           0.128960   

   ko18sc_merged-rep  
0           0.419253  
1           0.639234  
2           0.475328  
3           0.149203  
4           0.000499  
  external_gene_name   wt11fb1   wt11fb2   wt13fb1   wt13fb2   wt15fb1  \
0              Foxg1  0.922886  0.867472  0.926063  0.939823  0.839176   
1               Lhx6  0.491527  0.486385  0.506861  0.531180  0.437761   
2               Tbr1  0.801779  0.751527  0.778444  0.791757  0.679817   
3               Emx1  0.718767  0.704467  0.676680  0.681688  0.509412   
4              Eomes  0.734863  0.709996  0.744143  0.753513  0.627795   

    wt15fb2   wt18fb1   wt18fb2   wt11mb1  ...   wt18hb1   wt18hb2   wt11sc1  \
0  0.808620  0.723653  0.813484  0.658203  ...  0.081689  0.071425  0.249848   
1  0.426002  0.407807  0.454968  0.332904  ...  0.098612  0.074303  0.160607   
2  0.656994  0.589008  0.671771  0.532376  ...  0.088905  0.072286  0.248531   
3  0.542575  0.390703  0.474355  0.423804  ...  0.014109  0.008236  0.166470   
4  0.631606  0.528137  0.608344  0.461561  ...  0.026204  0.017905  0.138835   

    wt11sc2   wt13sc1   wt13sc2   wt15sc1   wt15sc2   wt18sc1   wt18sc2  
0  0.310349  0.061132  0.066563  0.161465  0.088042  0.041122  0.046668  
1  0.185424  0.022281  0.039226  0.135866  0.080810  0.057271  0.053055  
2  0.293322  0.046526  0.065346  0.168702  0.099231  0.049205  0.054317  
3  0.189083  0.012640  0.011746  0.057773  0.020403  0.000000  0.000494  
4  0.168011  0.010386  0.012669  0.072362  0.027627  0.003561  0.005906  

[5 rows x 33 columns]
  external_gene_name      VAE0      VAE1      VAE2
0              Foxg1  1.439024 -0.253037  2.145944
1               Lhx6  0.671850 -0.449539  1.003989
2               Tbr1  1.081165 -0.202013  1.451507
3               Emx1  0.687129 -0.087436  2.476957
4              Eomes  0.960182 -0.289618  2.436453
  external_gene_name  wt11fb_merged-rep  wt13fb_merged-rep  wt15fb_merged-rep  \
0               Hes5           0.651925           0.732021           0.321306   
1               Sox3           0.950178           0.927460           0.611877   
2             Snap25           0.000000           0.462183           0.653361   
3               Aqp4           0.086883           0.119506           0.136423   

   wt18fb_merged-rep  wt11mb_merged-rep  wt13mb_merged-rep  wt15mb_merged-rep  \
0           0.193179           1.000000           0.900866           0.299143   
1           0.358576           1.000000           0.755177           0.356305   
2           0.769478           0.286141           0.588509           0.742255   
3           0.447838           0.081333           0.192775           0.310377   

   wt18mb_merged-rep  wt11hb_merged-rep  wt13hb_merged-rep  wt15hb_merged-rep  \
0           0.280754           0.843860           0.450375           0.273479   
1           0.370077           0.935815           0.379595           0.191047   
2           0.835250           0.405914           0.744685           0.923558   
3           0.684133           0.167901           0.364807           0.562377   

   wt18hb_merged-rep  wt11sc_merged-rep  wt13sc_merged-rep  wt15sc_merged-rep  \
0           0.301305           0.623341           0.463581           0.000000   
1           0.258589           0.774401           0.244539           0.000000   
2           0.988208           0.393498           0.746979           0.836024   
3           0.947851           0.000000           0.192491           0.512780   

   wt18sc_merged-rep  
0           0.210562  
1           0.150160  
2           1.000000  
3           1.000000  
  external_gene_name  ko11fb_merged-rep  ko13fb_merged-rep  ko15fb_merged-rep  \
0               Hes5           0.980069           0.589436           0.097420   
1               Sox3           1.000000           0.511926           0.243986   
2             Snap25           0.000000           0.642975           0.743322   
3               Aqp4           0.095491           0.233419           0.510020   

   ko18fb_merged-rep  ko11mb_merged-rep  ko13mb_merged-rep  ko15mb_merged-rep  \
0           0.157003           1.000000           0.453238           0.000000   
1           0.136114           0.924000           0.277327           0.051959   
2           0.817719           0.210821           0.612830           0.721238   
3           0.784045           0.100108           0.340701           0.582141   

   ko18mb_merged-rep  ko11hb_merged-rep  ko13hb_merged-rep  ko15hb_merged-rep  \
0           0.224047           0.972094           0.104580           0.046064   
1           0.137127           0.996363           0.046774           0.000000   
2           0.833872           0.071700           0.714730           0.851133   
3           0.906933           0.110199           0.441951           0.752616   

   ko18hb_merged-rep  ko11sc_merged-rep  ko13sc_merged-rep  ko15sc_merged-rep  \
0           0.205237           0.830015           0.311043           0.132207   
1           0.024383           0.852967           0.169373           0.088402   
2           0.991832           0.304518           0.729397           0.867826   
3           0.974931           0.000000           0.330142           0.694683   

   ko18sc_merged-rep  
0           0.199092  
1           0.141788  
2           1.000000  
3           1.000000  
  external_gene_name   wt11fb1   wt11fb2   wt13fb1   wt13fb2   wt15fb1  \
0               Hes5  0.981817  0.987228  0.973498  0.976594  0.830756   
1               Sox3  0.970875  0.992218  0.930620  0.918513  0.767600   
2             Snap25  0.363120  0.316262  0.657150  0.698198  0.725568   
3               Aqp4  0.038557  0.036261  0.220066  0.272300  0.440949   

    wt15fb2   wt18fb1   wt18fb2   wt11mb1  ...   wt18hb1   wt18hb2   wt11sc1  \
0  0.788426  0.712080  0.810452  0.957149  ...  0.549773  0.582186  0.889913   
1  0.715014  0.546623  0.663956  0.901215  ...  0.376874  0.417595  0.938277   
2  0.691256  0.758497  0.779823  0.532950  ...  0.985842  0.968733  0.662778   
3  0.392164  0.680898  0.637748  0.071929  ...  0.877799  0.774730  0.053030   

    wt11sc2   wt13sc1   wt13sc2   wt15sc1   wt15sc2   wt18sc1   wt18sc2  
0  0.918254  0.618061  0.647526  0.634290  0.587524  0.579876  0.597228  
1  0.945064  0.538143  0.526038  0.577532  0.466550  0.426721  0.429964  
2  0.630526  0.878100  0.902368  0.896805  0.932839  0.991296  0.989390  
3  0.046210  0.269002  0.335006  0.424345  0.579496  0.856116  0.796833  

[4 rows x 33 columns]
<Figure size 360x360 with 0 Axes>
  external_gene_name   ko11fb1   ko11fb2   ko13fb1   ko13fb2   ko15fb1  \
0               Sox3  0.808370  0.850944  0.634469  0.641140  0.436497   
1              Foxg1  0.587418  0.844399  0.675516  0.716001  0.568492   
2                En2  0.540933  0.425117  0.482241  0.458814  0.407802   
3              Hoxc9  0.259467  0.000955  0.630115  0.631224  0.468859   

    ko15fb2   ko18fb1   ko18fb2   ko11mb1  ...   ko18hb1   ko18hb2   ko11sc1  \
0  0.467963  0.294673  0.363380  0.782356  ...  0.313063  0.323041  0.773846   
1  0.587334  0.392014  0.474526  0.443367  ...  0.269765  0.270670  0.307034   
2  0.423135  0.303470  0.379362  0.583968  ...  0.401391  0.399918  0.553433   
3  0.522189  0.309474  0.386288  0.449400  ...  0.425606  0.467897  0.712405   

    ko11sc2   ko13sc1   ko13sc2   ko15sc1   ko15sc2   ko18sc1   ko18sc2  
0  0.812112  0.366856  0.413646  0.351001  0.361990  0.342139  0.371337  
1  0.278334  0.198134  0.196116  0.220290  0.173314  0.222570  0.214311  
2  0.526261  0.404720  0.409001  0.394354  0.388792  0.343093  0.369912  
3  0.769670  0.656655  0.681164  0.561543  0.612212  0.500753  0.535793  

[4 rows x 33 columns]
<Figure size 144x144 with 0 Axes>
  external_gene_name   wt11fb1   wt11fb2   wt13fb1   wt13fb2   wt15fb1  \
0               Sox3  0.835937  0.853705  0.802427  0.792347  0.666717   
1              Foxg1  0.861245  0.812161  0.864059  0.876246  0.787098   
2                En2  0.406390  0.470401  0.317830  0.349386  0.219831   
3              Hoxc9  0.000748  0.001360  0.000415  0.000424  0.000117   

    wt15fb2   wt18fb1   wt18fb2   wt11mb1  ...   wt18hb1   wt18hb2   wt11sc1  \
0  0.622941  0.482761  0.580437  0.777947  ...  0.341451  0.375350  0.808801   
1  0.760034  0.684773  0.764342  0.626801  ...  0.116152  0.107060  0.265099   
2  0.218171  0.181988  0.216225  0.528045  ...  0.328226  0.328117  0.538796   
3  0.000351  0.000109  0.000130  0.012668  ...  0.077109  0.216570  0.762355   

    wt11sc2   wt13sc1   wt13sc2   wt15sc1   wt15sc2   wt18sc1   wt18sc2  
0  0.814451  0.475702  0.465625  0.508492  0.416103  0.382946  0.385646  
1  0.318688  0.097944  0.102754  0.186814  0.121779  0.080220  0.085132  
2  0.547716  0.396924  0.406313  0.381318  0.339458  0.283468  0.305117  
3  0.732512  0.448066  0.638906  0.531419  0.555274  0.478245  0.510027  

[4 rows x 33 columns]
  external_gene_name  log2FoldChange_fb  log2FoldChange_mb  log2FoldChange_hb  \
0               Sox3           0.051694           0.063570           0.106352   
1              Foxg1           0.053807           0.146351           0.434524   
2                En2           0.316034           0.178869           0.197792   
3              Hoxc9           0.976166           0.943728           0.698286   

   log2FoldChange_sc  log2FoldChange_a11  log2FoldChange_a13  \
0           0.101062            0.166824            0.075052   
1           0.494195            0.043796            0.060901   
2           0.172999            0.277248            0.216414   
3           0.307727            0.955366            0.949735   

   log2FoldChange_a15  log2FoldChange_a18  log2FoldChange_p11  \
0            0.090404            0.096041            0.199159   
1            0.133057            0.130258            0.472935   
2            0.266078            0.295602            0.094749   
3            0.946570            0.951769            0.695376   

   log2FoldChange_p13  log2FoldChange_p15  log2FoldChange_p18  
0            0.087311            0.097999            0.148632  
1            0.414733            0.462467            0.473782  
2            0.174986            0.207440            0.222633  
3            0.564006            0.491801            0.514690  
  external_gene_name  \
0               Sox3   
1              Foxg1   
2                En2   
3              Hoxc9   

   forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal_log2  \
0                                           0.307884                         
1                                           0.627001                         
2                                           0.829210                         
3                                           1.000000                         

   forebrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF592HST_signal_log2  \
0                                           0.042487                         
1                                           0.529583                         
2                                           0.823684                         
3                                           0.950957                         

   forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal_log2  \
0                                           0.211108                         
1                                           0.600815                         
2                                           0.820386                         
3                                           0.943111                         

   forebrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF076ZNJ_signal_log2  \
0                                           0.155030                         
1                                           0.551627                         
2                                           0.827545                         
3                                           0.988310                         

   forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal_log2  \
0                                           0.043732                         
1                                           0.643342                         
2                                           0.877358                         
3                                           0.949459                         

   forebrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF082ONF_signal_log2  \
0                                           0.095107                         
1                                           0.498344                         
2                                           0.787005                         
3                                           0.950551                         

   forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal_log2  \
0                                           0.040961                         
1                                           0.498511                         
2                                           0.776713                         
3                                           0.845407                         

   hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal_log2  \
0                                           0.279632                         
1                                           0.702619                         
2                                           0.830685                         
3                                           1.000000                         

   hindbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF809JLW_signal_log2  \
0                                           0.053879                         
1                                           0.747898                         
2                                           0.802400                         
3                                           0.914135                         

   ...  \
0  ...   
1  ...   
2  ...   
3  ...   

   hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal_log2  \
0                                           0.197014                         
1                                           0.884408                         
2                                           0.865631                         
3                                           0.975048                         

   hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal_log2  \
0                                           0.255986                         
1                                           0.929549                         
2                                           0.877201                         
3                                           1.000000                         

   hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal_log2  \
0                                           0.027715                         
1                                           0.800277                         
2                                           0.843778                         
3                                           0.886447                         

   midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal_log2  \
0                                           0.314673                        
1                                           0.635946                        
2                                           0.839909                        
3                                           1.000000                        

   midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal_log2  \
0                                           0.101360                        
1                                           0.657623                        
2                                           0.804888                        
3                                           0.989989                        

   midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal_log2  \
0                                           0.357026                        
1                                           0.675749                        
2                                           0.835075                        
3                                           0.966018                        

   midbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF276UEL_signal_log2  \
0                                           0.323529                        
1                                           0.704756                        
2                                           0.840373                        
3                                           0.990771                        

   midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal_log2  \
0                                           0.340934                        
1                                           0.853614                        
2                                           0.864859                        
3                                           0.988904                        

   midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal_log2  \
0                                           0.315893                        
1                                           0.742814                        
2                                           0.804218                        
3                                           0.971802                        

   midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal_log2  
0                                           0.082138                       
1                                           0.794061                       
2                                           0.863890                       
3                                           0.958068                       

[4 rows x 22 columns]
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/matrix.py:1205: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. 
  self.fig.tight_layout(**tight_params)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/scivae/vis.py:351: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  fig = plt.figure()
  external_gene_name   ko11fb1  ko11fb2   ko13fb1   ko13fb2   ko15fb1  \
0               Sox3  0.938785      1.0  0.490566  0.515019  0.282135   
1              Foxg1  0.399059      1.0  0.864240  0.907562  0.796720   
2                En2  0.999684      0.0  0.568539  0.578491  0.459351   
3              Hoxc9  0.332753      0.0  0.942823  0.956517  0.786817   

    ko15fb2   ko18fb1   ko18fb2   ko11mb1  ...   ko18hb1   ko18hb2   ko11sc1  \
0  0.211148  0.128313  0.158717  0.803567  ...  0.004166  0.069234  0.781372   
1  0.790024  0.760058  0.764653  0.424918  ...  0.533096  0.532798  0.127241   
2  0.476414  0.399936  0.469709  0.815591  ...  0.479072  0.534291  0.410039   
3  0.795932  0.650076  0.710400  0.552940  ...  0.589719  0.624295  1.000000   

    ko11sc2   ko13sc1   ko13sc2   ko15sc1   ko15sc2   ko18sc1   ko18sc2  
0  0.876284  0.152762  0.197861  0.077266  0.118539  0.139331  0.158548  
1  0.000000  0.369131  0.308179  0.428317  0.363157  0.430157  0.386570  
2  0.081347  0.564060  0.568928  0.516123  0.428650  0.405513  0.415569  
3  0.968066  0.899169  0.913084  0.757540  0.819714  0.760162  0.752785  

[4 rows x 33 columns]
<Figure size 360x360 with 0 Axes>
  external_gene_name   wt11fb1   wt11fb2   wt13fb1   wt13fb2   wt15fb1  \
0               Sox3  0.987655  0.888969  0.919341  0.914714  0.548995   
1              Foxg1  0.905497  0.839103  0.962206  0.971014  0.989059   
2                En2  0.021750  0.076919  0.006142  0.011685  0.030719   
3              Hoxc9  0.016999  0.005155  0.000000  0.006330  0.000000   

    wt15fb2   wt18fb1   wt18fb2   wt11mb1  ...   wt18hb1   wt18hb2   wt11sc1  \
0  0.693739  0.294510  0.473605  1.000000  ...  0.320858  0.259908  0.760646   
1  1.000000  0.972821  0.990432  0.366511  ...  0.057609  0.041185  0.055598   
2  0.032995  0.030337  0.048266  0.929953  ...  0.106980  0.112892  0.044118   
3  0.035905  0.021010  0.013906  0.012152  ...  0.010465  0.473454  0.926013   

    wt11sc2   wt13sc1   wt13sc2   wt15sc1   wt15sc2   wt18sc1   wt18sc2  
0  0.786617  0.276225  0.278214  0.000000  0.096237  0.216120  0.161478  
1  0.057279  0.011313  0.016418  0.013319  0.000000  0.006830  0.027252  
2  0.067930  0.009606  0.027778  0.011913  0.015164  0.019509  0.014426  
3  0.919263  1.000000  0.995818  0.784096  0.892346  0.852279  0.898904  

[4 rows x 33 columns]
['forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal', 'forebrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF592HST_signal', 'forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal', 'forebrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF076ZNJ_signal', 'forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal', 'forebrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF082ONF_signal', 'forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal', 'hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal', 'hindbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF809JLW_signal', 'hindbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF435HPM_signal', 'hindbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF324KBG_signal', 'hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal', 'hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal', 'hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal', 'midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal', 'midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal', 'midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal', 'midbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF276UEL_signal', 'midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal', 'midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal', 'midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal']
  external_gene_name  \
0               Sox3   
1              Foxg1   
2                En2   
3              Hoxc9   

   forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal  \
0                                           1.000000                    
1                                           0.983556                    
2                                           1.000000                    
3                                           1.000000                    

   forebrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF592HST_signal  \
0                                           0.000000                    
1                                           0.286343                    
2                                           0.100743                    
3                                           0.283206                    

   forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal  \
0                                           0.555604                    
1                                           0.308459                    
2                                           0.249711                    
3                                           0.428984                    

   forebrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF076ZNJ_signal  \
0                                           0.000000                    
1                                           0.000000                    
2                                           0.434323                    
3                                           0.244270                    

   forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal  \
0                                           0.000000                    
1                                           0.309227                    
2                                           0.158969                    
3                                           0.231445                    

   forebrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF082ONF_signal  \
0                                           0.000000                    
1                                           0.000000                    
2                                           0.049647                    
3                                           0.107695                    

   forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal  \
0                                           0.000000                    
1                                           0.000000                    
2                                           0.125367                    
3                                           0.000000                    

   hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal  \
0                                           0.654686                    
1                                           0.983348                    
2                                           0.558443                    
3                                           0.897566                    

   hindbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF809JLW_signal  ...  \
0                                           0.000000                   ...   
1                                           0.538108                   ...   
2                                           0.135460                   ...   
3                                           0.272632                   ...   

   hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal  \
0                                           0.541669                    
1                                           0.852510                    
2                                           0.168170                    
3                                           0.260769                    

   hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal  \
0                                           0.000000                    
1                                           0.597450                    
2                                           0.052538                    
3                                           0.168848                    

   hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal  \
0                                           0.000000                    
1                                           0.483976                    
2                                           0.011061                    
3                                           0.134280                    

   midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal  \
0                                           0.660801                   
1                                           1.000000                   
2                                           0.534473                   
3                                           0.610359                   

   midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal  \
0                                           0.000000                   
1                                           0.865415                   
2                                           0.049138                   
3                                           0.369078                   

   midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal  \
0                                           0.000000                   
1                                           0.966515                   
2                                           0.270990                   
3                                           0.524347                   

   midbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF276UEL_signal  \
0                                           0.710406                   
1                                           0.795924                   
2                                           0.225399                   
3                                           0.206282                   

   midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal  \
0                                           0.000000                   
1                                           0.607118                   
2                                           0.117276                   
3                                           0.257075                   

   midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal  \
0                                           0.000000                   
1                                           0.414332                   
2                                           0.016693                   
3                                           0.112401                   

   midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal  
0                                           0.000000                  
1                                           0.537933                  
2                                           0.000000                  
3                                           0.073440                  

[4 rows x 22 columns]
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/matrix.py:1205: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. 
  self.fig.tight_layout(**tight_params)
['log2FoldChange_fb', 'log2FoldChange_mb', 'log2FoldChange_hb', 'log2FoldChange_sc', 'log2FoldChange_a11', 'log2FoldChange_a13', 'log2FoldChange_a15', 'log2FoldChange_a18', 'log2FoldChange_p11', 'log2FoldChange_p13', 'log2FoldChange_p15', 'log2FoldChange_p18']
  external_gene_name  log2FoldChange_fb  log2FoldChange_mb  log2FoldChange_hb  \
0               Sox3           0.186678           0.215256           0.403930   
1              Foxg1           0.000000           0.969118           0.883091   
2                En2           0.875505           0.000000           0.751336   
3              Hoxc9           0.774984           0.905491           0.674293   

   log2FoldChange_sc  log2FoldChange_a11  log2FoldChange_a13  \
0           0.965154            0.839535            0.000000   
1           1.000000            0.211348            0.616524   
2           0.938987            0.510267            0.531121   
3           0.000000            0.563688            1.000000   

   log2FoldChange_a15  log2FoldChange_a18  log2FoldChange_p11  \
0            0.210824            0.444475            1.000000   
1            0.515227            0.310291            0.603990   
2            0.413526            0.393134            0.291812   
3            0.765863            0.740117            0.298132   

   log2FoldChange_p13  log2FoldChange_p15  log2FoldChange_p18  
0            0.544195            0.863352            0.689699  
1            0.883013            0.979874            0.953042  
2            0.795116            1.000000            0.741950  
3            0.445875            0.373453            0.100662  
  external_gene_name  ko11fb_merged-rep  ko13fb_merged-rep  ko15fb_merged-rep  \
0               Sox1                1.0           0.716547           0.494781   
1               Sox2                1.0           0.629236           0.294420   
2               Sox3                1.0           0.511926           0.243986   

   ko18fb_merged-rep  ko11mb_merged-rep  ko13mb_merged-rep  ko15mb_merged-rep  \
0           0.460824           0.723482           0.314919           0.235458   
1           0.292689           0.832062           0.463111           0.152282   
2           0.136114           0.924000           0.277327           0.051959   

   ko18mb_merged-rep  ko11hb_merged-rep  ko13hb_merged-rep  ko15hb_merged-rep  \
0           0.241585           0.899023           0.001515           0.077447   
1           0.188908           0.937412           0.129238           0.000000   
2           0.137127           0.996363           0.046774           0.000000   

   ko18hb_merged-rep  ko11sc_merged-rep  ko13sc_merged-rep  ko15sc_merged-rep  \
0           0.130070           0.202840           0.042317           0.000000   
1           0.066738           0.787053           0.182959           0.011233   
2           0.024383           0.852967           0.169373           0.088402   

   ko18sc_merged-rep  
0           0.113725  
1           0.162649  
2           0.141788  
  external_gene_name  wt11fb_merged-rep  wt13fb_merged-rep  wt15fb_merged-rep  \
0               Sox1           0.911189           1.000000           0.836312   
1               Sox2           0.943380           0.949568           0.707070   
2               Sox3           0.950178           0.927460           0.611877   

   wt18fb_merged-rep  wt11mb_merged-rep  wt13mb_merged-rep  wt15mb_merged-rep  \
0           0.786355           0.932404           0.751252           0.426557   
1           0.550415           1.000000           0.622615           0.217100   
2           0.358576           1.000000           0.755177           0.356305   

   wt18mb_merged-rep  wt11hb_merged-rep  wt13hb_merged-rep  wt15hb_merged-rep  \
0           0.474636           0.800309           0.481115           0.281785   
1           0.375949           0.853657           0.468725           0.236694   
2           0.370077           0.935815           0.379595           0.191047   

   wt18hb_merged-rep  wt11sc_merged-rep  wt13sc_merged-rep  wt15sc_merged-rep  \
0           0.232632           0.417540           0.215308                0.0   
1           0.221981           0.794292           0.420991                0.0   
2           0.258589           0.774401           0.244539                0.0   

   wt18sc_merged-rep  
0           0.126464  
1           0.230233  
2           0.150160  
  external_gene_name      VAE0      VAE1      VAE2
0               Sox3  1.946739  0.479508  0.929930
1              Foxg1  1.439024 -0.253037  2.145944
2                En2  0.859806 -1.007098  0.235467
3              Hoxc9 -1.507013 -3.432837 -1.692456
H3K4me1 H3K4me1 1.0 0.0
H3K4me1 H3K4me2 0.5778097092631771 6.30995115822728e-123
H3K4me1 H3K4me3 0.5424054244750087 1.0462500992593941e-105
H3K4me1 H3K27ac 0.4474397884575597 1.868776646979828e-68
H3K4me1 H3K36me3 0.23331708205528487 2.0919177335021665e-18
H3K4me1 H3K27me3 0.4183583143974251 3.2720909943747853e-59
H3K4me1 Log2FC FB -0.005828660227440098 0.829281719129814
H3K4me1 Log2FC MB -0.07363958460652882 0.006374445946102697
H3K4me1 Log2FC HB -0.12856724647180287 1.7885836117251365e-06
H3K4me1 Log2FC SC 0.01896101107434295 0.48299579643865487
H3K4me1 VAE0 0.29031611063142665 4.930166724775107e-28
H3K4me1 VAE1 -0.4294316663802427 1.2627522245545507e-62
H3K4me1 VAE2 0.1608669464307792 2.097868335239766e-09
H3K4me2 H3K4me1 0.5778097092631771 6.30995115822728e-123
H3K4me2 H3K4me2 1.0 0.0
H3K4me2 H3K4me3 0.9249576243822666 0.0
H3K4me2 H3K27ac 0.5809337496355107 1.505979846743912e-124
H3K4me2 H3K36me3 0.2978871153402344 1.7085900450507975e-29
H3K4me2 H3K27me3 0.32314796735960055 1.0851872748676937e-34
H3K4me2 Log2FC FB -0.09022151932051348 0.0008245804102662129
H3K4me2 Log2FC MB -0.18060329215713622 1.6216971012303665e-11
H3K4me2 Log2FC HB -0.2789470609848839 6.36148954888253e-26
H3K4me2 Log2FC SC -0.08115312156234512 0.0026377199644407583
H3K4me2 VAE0 0.41307606418431353 1.255080109427997e-57
H3K4me2 VAE1 -0.3459967816690149 7.715291848745455e-40
H3K4me2 VAE2 0.0547856481985845 0.042537576997147844
H3K4me3 H3K4me1 0.5424054244750087 1.0462500992593941e-105
H3K4me3 H3K4me2 0.9249576243822665 0.0
H3K4me3 H3K4me3 1.0 0.0
H3K4me3 H3K27ac 0.6651601396750653 7.046953869800731e-176
H3K4me3 H3K36me3 0.38465069892378145 1.4060008082927108e-49
H3K4me3 H3K27me3 0.28080152722934676 2.9236513393994367e-26
H3K4me3 Log2FC FB -0.1552552713147644 7.538405719288966e-09
H3K4me3 Log2FC MB -0.25008860580419645 5.3855237481581464e-21
H3K4me3 Log2FC HB -0.33927820537868725 2.7958816066164293e-38
H3K4me3 Log2FC SC -0.1462532917403755 5.338305069703614e-08
H3K4me3 VAE0 0.5049970332540887 1.2931183112619104e-89
H3K4me3 VAE1 -0.29554922126419003 4.8781949636658434e-29
H3K4me3 VAE2 0.0824669217392564 0.0022436715756004757
H3K27ac H3K4me1 0.44743978845755966 1.868776646980095e-68
H3K27ac H3K4me2 0.5809337496355107 1.505979846743912e-124
H3K27ac H3K4me3 0.6651601396750653 7.046953869800731e-176
H3K27ac H3K27ac 1.0 0.0
H3K27ac H3K36me3 0.40924313296843956 1.699390424794447e-56
H3K27ac H3K27me3 0.1335242473543216 6.986885390846781e-07
H3K27ac Log2FC FB -0.1895952937422755 1.462114117086756e-12
H3K27ac Log2FC MB -0.27333105823559734 6.462122594893282e-25
H3K27ac Log2FC HB -0.34523770917786767 1.162608260691108e-39
H3K27ac Log2FC SC -0.19369439965896984 4.689838803083501e-13
H3K27ac VAE0 0.565460701897515 1.1067285645987936e-116
H3K27ac VAE1 -0.13974158340089593 2.0471583971385752e-07
H3K27ac VAE2 0.02811476348996577 0.29821822708243745
H3K36me3 H3K4me1 0.23331708205528484 2.0919177335021665e-18
H3K36me3 H3K4me2 0.2978871153402344 1.7085900450507975e-29
H3K36me3 H3K4me3 0.38465069892378145 1.4060008082927108e-49
H3K36me3 H3K27ac 0.40924313296843967 1.6993904247943983e-56
H3K36me3 H3K36me3 1.0 0.0
H3K36me3 H3K27me3 -0.060811738887076326 0.024340755777373508
H3K36me3 Log2FC FB -0.22787654840262245 1.3131908798832538e-17
H3K36me3 Log2FC MB -0.29142012227610314 3.038589462812012e-28
H3K36me3 Log2FC HB -0.27428074788659457 4.382946347360937e-25
H3K36me3 Log2FC SC -0.23820189269891243 3.861529498749434e-19
H3K36me3 VAE0 0.5128226801227292 8.11907865775757e-93
H3K36me3 VAE1 0.04696623939013081 0.08214161188440461
H3K36me3 VAE2 0.016248697834837858 0.5477522547037476
H3K27me3 H3K4me1 0.4183583143974251 3.2720909943747853e-59
H3K27me3 H3K4me2 0.32314796735960055 1.0851872748676937e-34
H3K27me3 H3K4me3 0.28080152722934676 2.9236513393994367e-26
H3K27me3 H3K27ac 0.13352424735432158 6.986885390846871e-07
H3K27me3 H3K36me3 -0.060811738887076326 0.024340755777373508
H3K27me3 H3K27me3 1.0 0.0
H3K27me3 Log2FC FB 0.34922344775478703 1.3330712289599865e-40
H3K27me3 Log2FC MB 0.21994143742472344 1.7596945962900848e-16
H3K27me3 Log2FC HB 0.05577417477278321 0.03893482554600838
H3K27me3 Log2FC SC 0.2297418333448438 7.032519975432944e-18
H3K27me3 VAE0 0.03803275569023637 0.15928977898633345
H3K27me3 VAE1 -0.9347886653056977 0.0
H3K27me3 VAE2 0.0635811903302392 0.018549792310585875
Log2FC FB H3K4me1 -0.005828660227440098 0.829281719129814
Log2FC FB H3K4me2 -0.09022151932051346 0.0008245804102662144
Log2FC FB H3K4me3 -0.1552552713147644 7.538405719288966e-09
Log2FC FB H3K27ac -0.1895952937422755 1.462114117086756e-12
Log2FC FB H3K36me3 -0.22787654840262248 1.3131908798832538e-17
Log2FC FB H3K27me3 0.349223447754787 1.3330712289599865e-40
Log2FC FB Log2FC FB 1.0 0.0
Log2FC FB Log2FC MB 0.4874154790987053 1.0212500251884752e-82
Log2FC FB Log2FC HB 0.2538593319769123 1.323350676401945e-21
Log2FC FB Log2FC SC 0.3847565054541962 1.316627432201345e-49
Log2FC FB VAE0 -0.35659342295581337 2.2360651454584055e-42
Log2FC FB VAE1 -0.42546598386238704 2.180071569852324e-61
Log2FC FB VAE2 -0.2673799783130145 7.108362262493554e-24
Log2FC MB H3K4me1 -0.07363958460652881 0.0063744459461027095
Log2FC MB H3K4me2 -0.18060329215713622 1.6216971012303665e-11
Log2FC MB H3K4me3 -0.2500886058041965 5.385523748158109e-21
Log2FC MB H3K27ac -0.27333105823559734 6.462122594893282e-25
Log2FC MB H3K36me3 -0.29142012227610314 3.038589462812012e-28
Log2FC MB H3K27me3 0.2199414374247234 1.7596945962901607e-16
Log2FC MB Log2FC FB 0.48741547909870536 1.0212500251884752e-82
Log2FC MB Log2FC MB 1.0 0.0
Log2FC MB Log2FC HB 0.5825485565927759 2.1497777521472687e-125
Log2FC MB Log2FC SC 0.41836646559213686 3.253563739871815e-59
Log2FC MB VAE0 -0.4109226755999542 5.448112954456422e-57
Log2FC MB VAE1 -0.23624776872255865 7.626021772616763e-19
Log2FC MB VAE2 -0.02289057494832727 0.39704663424441755
Log2FC HB H3K4me1 -0.1285672464718029 1.788583611725127e-06
Log2FC HB H3K4me2 -0.278947060984884 6.361489548882348e-26
Log2FC HB H3K4me3 -0.33927820537868725 2.7958816066164293e-38
Log2FC HB H3K27ac -0.34523770917786767 1.162608260691108e-39
Log2FC HB H3K36me3 -0.27428074788659457 4.382946347360937e-25
Log2FC HB H3K27me3 0.05577417477278321 0.03893482554600838
Log2FC HB Log2FC FB 0.2538593319769123 1.323350676401945e-21
Log2FC HB Log2FC MB 0.5825485565927759 2.1497777521472687e-125
Log2FC HB Log2FC HB 1.0 0.0
Log2FC HB Log2FC SC 0.4916008496130506 2.532062750739362e-84
Log2FC HB VAE0 -0.3358407592675868 1.696244835667893e-37
Log2FC HB VAE1 0.000893725395473486 0.9736252988078797
Log2FC HB VAE2 0.16871385420091436 3.2493899832182813e-10
Log2FC SC H3K4me1 0.01896101107434295 0.48299579643865487
Log2FC SC H3K4me2 -0.08115312156234512 0.0026377199644407583
Log2FC SC H3K4me3 -0.1462532917403755 5.338305069703614e-08
Log2FC SC H3K27ac -0.19369439965896987 4.689838803083501e-13
Log2FC SC H3K36me3 -0.23820189269891243 3.861529498749434e-19
Log2FC SC H3K27me3 0.2297418333448438 7.032519975432944e-18
Log2FC SC Log2FC FB 0.38475650545419626 1.3166274322012883e-49
Log2FC SC Log2FC MB 0.4183664655921368 3.253563739871815e-59
Log2FC SC Log2FC HB 0.4916008496130506 2.532062750739362e-84
Log2FC SC Log2FC SC 1.0 0.0
Log2FC SC VAE0 -0.30086852336773884 4.42036845192071e-30
Log2FC SC VAE1 -0.19659919987030303 2.0632386010018646e-13
Log2FC SC VAE2 0.0544089035764286 0.043982879996444985
VAE0 H3K4me1 0.29031611063142665 4.930166724775107e-28
VAE0 H3K4me2 0.4130760641843135 1.255080109427997e-57
VAE0 H3K4me3 0.5049970332540886 1.2931183112620578e-89
VAE0 H3K27ac 0.565460701897515 1.1067285645987936e-116
VAE0 H3K36me3 0.5128226801227292 8.11907865775757e-93
VAE0 H3K27me3 0.03803275569023637 0.15928977898633345
VAE0 Log2FC FB -0.3565934229558133 2.2360651454584692e-42
VAE0 Log2FC MB -0.41092267559995416 5.448112954456422e-57
VAE0 Log2FC HB -0.33584075926758683 1.696244835667772e-37
VAE0 Log2FC SC -0.30086852336773884 4.42036845192071e-30
VAE0 VAE0 1.0 0.0
VAE0 VAE1 -0.044440792633740046 0.10000658140919086
VAE0 VAE2 -0.06607105313289889 0.014410819464438108
VAE1 H3K4me1 -0.42943166638024277 1.2627522245545507e-62
VAE1 H3K4me2 -0.3459967816690149 7.715291848745455e-40
VAE1 H3K4me3 -0.29554922126419003 4.8781949636658434e-29
VAE1 H3K27ac -0.13974158340089593 2.0471583971385752e-07
VAE1 H3K36me3 0.0469662393901308 0.08214161188440466
VAE1 H3K27me3 -0.9347886653056977 0.0
VAE1 Log2FC FB -0.4254659838623871 2.180071569852324e-61
VAE1 Log2FC MB -0.23624776872255862 7.626021772616817e-19
VAE1 Log2FC HB 0.0008937253954734859 0.9736252988078797
VAE1 Log2FC SC -0.19659919987030305 2.0632386010018648e-13
VAE1 VAE0 -0.044440792633740046 0.10000658140919086
VAE1 VAE1 1.0 0.0
VAE1 VAE2 -0.010831726378816332 0.6886318434581902
VAE2 H3K4me1 0.1608669464307792 2.097868335239766e-09
VAE2 H3K4me2 0.0547856481985845 0.042537576997147844
VAE2 H3K4me3 0.08246692173925642 0.0022436715756004757
VAE2 H3K27ac 0.02811476348996577 0.29821822708243745
VAE2 H3K36me3 0.016248697834837858 0.5477522547037476
VAE2 H3K27me3 0.06358119033023919 0.01854979231058591
VAE2 Log2FC FB -0.2673799783130146 7.108362262493199e-24
VAE2 Log2FC MB -0.02289057494832727 0.39704663424441755
VAE2 Log2FC HB 0.16871385420091436 3.2493899832182813e-10
VAE2 Log2FC SC 0.0544089035764286 0.043982879996444985
VAE2 VAE0 -0.06607105313289889 0.014410819464438108
VAE2 VAE1 -0.010831726378816332 0.6886318434581902
VAE2 VAE2 1.0 0.0
<Figure size 216x216 with 0 Axes>
H3K4me1 H3K4me1 1.0 0.0
H3K4me1 H3K4me2 0.5778097092631771 6.30995115822728e-123
H3K4me1 H3K4me3 0.5424054244750087 1.0462500992593941e-105
H3K4me1 H3K27ac 0.4474397884575597 1.868776646979828e-68
H3K4me1 H3K36me3 0.23331708205528487 2.0919177335021665e-18
H3K4me1 H3K27me3 0.4183583143974251 3.2720909943747853e-59
H3K4me1 Log2FC FB -0.005828660227440098 0.829281719129814
H3K4me1 Log2FC MB -0.07363958460652882 0.006374445946102697
H3K4me1 Log2FC HB -0.12856724647180287 1.7885836117251365e-06
H3K4me1 Log2FC SC 0.01896101107434295 0.48299579643865487
H3K4me1 VAE0 0.29031611063142665 4.930166724775107e-28
H3K4me1 VAE1 -0.4294316663802427 1.2627522245545507e-62
H3K4me1 VAE2 0.1608669464307792 2.097868335239766e-09
H3K4me2 H3K4me1 0.5778097092631771 6.30995115822728e-123
H3K4me2 H3K4me2 1.0 0.0
H3K4me2 H3K4me3 0.9249576243822666 0.0
H3K4me2 H3K27ac 0.5809337496355107 1.505979846743912e-124
H3K4me2 H3K36me3 0.2978871153402344 1.7085900450507975e-29
H3K4me2 H3K27me3 0.32314796735960055 1.0851872748676937e-34
H3K4me2 Log2FC FB -0.09022151932051348 0.0008245804102662129
H3K4me2 Log2FC MB -0.18060329215713622 1.6216971012303665e-11
H3K4me2 Log2FC HB -0.2789470609848839 6.36148954888253e-26
H3K4me2 Log2FC SC -0.08115312156234512 0.0026377199644407583
H3K4me2 VAE0 0.41307606418431353 1.255080109427997e-57
H3K4me2 VAE1 -0.3459967816690149 7.715291848745455e-40
H3K4me2 VAE2 0.0547856481985845 0.042537576997147844
H3K4me3 H3K4me1 0.5424054244750087 1.0462500992593941e-105
H3K4me3 H3K4me2 0.9249576243822665 0.0
H3K4me3 H3K4me3 1.0 0.0
H3K4me3 H3K27ac 0.6651601396750653 7.046953869800731e-176
H3K4me3 H3K36me3 0.38465069892378145 1.4060008082927108e-49
H3K4me3 H3K27me3 0.28080152722934676 2.9236513393994367e-26
H3K4me3 Log2FC FB -0.1552552713147644 7.538405719288966e-09
H3K4me3 Log2FC MB -0.25008860580419645 5.3855237481581464e-21
H3K4me3 Log2FC HB -0.33927820537868725 2.7958816066164293e-38
H3K4me3 Log2FC SC -0.1462532917403755 5.338305069703614e-08
H3K4me3 VAE0 0.5049970332540887 1.2931183112619104e-89
H3K4me3 VAE1 -0.29554922126419003 4.8781949636658434e-29
H3K4me3 VAE2 0.0824669217392564 0.0022436715756004757
H3K27ac H3K4me1 0.44743978845755966 1.868776646980095e-68
H3K27ac H3K4me2 0.5809337496355107 1.505979846743912e-124
H3K27ac H3K4me3 0.6651601396750653 7.046953869800731e-176
H3K27ac H3K27ac 1.0 0.0
H3K27ac H3K36me3 0.40924313296843956 1.699390424794447e-56
H3K27ac H3K27me3 0.1335242473543216 6.986885390846781e-07
H3K27ac Log2FC FB -0.1895952937422755 1.462114117086756e-12
H3K27ac Log2FC MB -0.27333105823559734 6.462122594893282e-25
H3K27ac Log2FC HB -0.34523770917786767 1.162608260691108e-39
H3K27ac Log2FC SC -0.19369439965896984 4.689838803083501e-13
H3K27ac VAE0 0.565460701897515 1.1067285645987936e-116
H3K27ac VAE1 -0.13974158340089593 2.0471583971385752e-07
H3K27ac VAE2 0.02811476348996577 0.29821822708243745
H3K36me3 H3K4me1 0.23331708205528484 2.0919177335021665e-18
H3K36me3 H3K4me2 0.2978871153402344 1.7085900450507975e-29
H3K36me3 H3K4me3 0.38465069892378145 1.4060008082927108e-49
H3K36me3 H3K27ac 0.40924313296843967 1.6993904247943983e-56
H3K36me3 H3K36me3 1.0 0.0
H3K36me3 H3K27me3 -0.060811738887076326 0.024340755777373508
H3K36me3 Log2FC FB -0.22787654840262245 1.3131908798832538e-17
H3K36me3 Log2FC MB -0.29142012227610314 3.038589462812012e-28
H3K36me3 Log2FC HB -0.27428074788659457 4.382946347360937e-25
H3K36me3 Log2FC SC -0.23820189269891243 3.861529498749434e-19
H3K36me3 VAE0 0.5128226801227292 8.11907865775757e-93
H3K36me3 VAE1 0.04696623939013081 0.08214161188440461
H3K36me3 VAE2 0.016248697834837858 0.5477522547037476
H3K27me3 H3K4me1 0.4183583143974251 3.2720909943747853e-59
H3K27me3 H3K4me2 0.32314796735960055 1.0851872748676937e-34
H3K27me3 H3K4me3 0.28080152722934676 2.9236513393994367e-26
H3K27me3 H3K27ac 0.13352424735432158 6.986885390846871e-07
H3K27me3 H3K36me3 -0.060811738887076326 0.024340755777373508
H3K27me3 H3K27me3 1.0 0.0
H3K27me3 Log2FC FB 0.34922344775478703 1.3330712289599865e-40
H3K27me3 Log2FC MB 0.21994143742472344 1.7596945962900848e-16
H3K27me3 Log2FC HB 0.05577417477278321 0.03893482554600838
H3K27me3 Log2FC SC 0.2297418333448438 7.032519975432944e-18
H3K27me3 VAE0 0.03803275569023637 0.15928977898633345
H3K27me3 VAE1 -0.9347886653056977 0.0
H3K27me3 VAE2 0.0635811903302392 0.018549792310585875
Log2FC FB H3K4me1 -0.005828660227440098 0.829281719129814
Log2FC FB H3K4me2 -0.09022151932051346 0.0008245804102662144
Log2FC FB H3K4me3 -0.1552552713147644 7.538405719288966e-09
Log2FC FB H3K27ac -0.1895952937422755 1.462114117086756e-12
Log2FC FB H3K36me3 -0.22787654840262248 1.3131908798832538e-17
Log2FC FB H3K27me3 0.349223447754787 1.3330712289599865e-40
Log2FC FB Log2FC FB 1.0 0.0
Log2FC FB Log2FC MB 0.4874154790987053 1.0212500251884752e-82
Log2FC FB Log2FC HB 0.2538593319769123 1.323350676401945e-21
Log2FC FB Log2FC SC 0.3847565054541962 1.316627432201345e-49
Log2FC FB VAE0 -0.35659342295581337 2.2360651454584055e-42
Log2FC FB VAE1 -0.42546598386238704 2.180071569852324e-61
Log2FC FB VAE2 -0.2673799783130145 7.108362262493554e-24
Log2FC MB H3K4me1 -0.07363958460652881 0.0063744459461027095
Log2FC MB H3K4me2 -0.18060329215713622 1.6216971012303665e-11
Log2FC MB H3K4me3 -0.2500886058041965 5.385523748158109e-21
Log2FC MB H3K27ac -0.27333105823559734 6.462122594893282e-25
Log2FC MB H3K36me3 -0.29142012227610314 3.038589462812012e-28
Log2FC MB H3K27me3 0.2199414374247234 1.7596945962901607e-16
Log2FC MB Log2FC FB 0.48741547909870536 1.0212500251884752e-82
Log2FC MB Log2FC MB 1.0 0.0
Log2FC MB Log2FC HB 0.5825485565927759 2.1497777521472687e-125
Log2FC MB Log2FC SC 0.41836646559213686 3.253563739871815e-59
Log2FC MB VAE0 -0.4109226755999542 5.448112954456422e-57
Log2FC MB VAE1 -0.23624776872255865 7.626021772616763e-19
Log2FC MB VAE2 -0.02289057494832727 0.39704663424441755
Log2FC HB H3K4me1 -0.1285672464718029 1.788583611725127e-06
Log2FC HB H3K4me2 -0.278947060984884 6.361489548882348e-26
Log2FC HB H3K4me3 -0.33927820537868725 2.7958816066164293e-38
Log2FC HB H3K27ac -0.34523770917786767 1.162608260691108e-39
Log2FC HB H3K36me3 -0.27428074788659457 4.382946347360937e-25
Log2FC HB H3K27me3 0.05577417477278321 0.03893482554600838
Log2FC HB Log2FC FB 0.2538593319769123 1.323350676401945e-21
Log2FC HB Log2FC MB 0.5825485565927759 2.1497777521472687e-125
Log2FC HB Log2FC HB 1.0 0.0
Log2FC HB Log2FC SC 0.4916008496130506 2.532062750739362e-84
Log2FC HB VAE0 -0.3358407592675868 1.696244835667893e-37
Log2FC HB VAE1 0.000893725395473486 0.9736252988078797
Log2FC HB VAE2 0.16871385420091436 3.2493899832182813e-10
Log2FC SC H3K4me1 0.01896101107434295 0.48299579643865487
Log2FC SC H3K4me2 -0.08115312156234512 0.0026377199644407583
Log2FC SC H3K4me3 -0.1462532917403755 5.338305069703614e-08
Log2FC SC H3K27ac -0.19369439965896987 4.689838803083501e-13
Log2FC SC H3K36me3 -0.23820189269891243 3.861529498749434e-19
Log2FC SC H3K27me3 0.2297418333448438 7.032519975432944e-18
Log2FC SC Log2FC FB 0.38475650545419626 1.3166274322012883e-49
Log2FC SC Log2FC MB 0.4183664655921368 3.253563739871815e-59
Log2FC SC Log2FC HB 0.4916008496130506 2.532062750739362e-84
Log2FC SC Log2FC SC 1.0 0.0
Log2FC SC VAE0 -0.30086852336773884 4.42036845192071e-30
Log2FC SC VAE1 -0.19659919987030303 2.0632386010018646e-13
Log2FC SC VAE2 0.0544089035764286 0.043982879996444985
VAE0 H3K4me1 0.29031611063142665 4.930166724775107e-28
VAE0 H3K4me2 0.4130760641843135 1.255080109427997e-57
VAE0 H3K4me3 0.5049970332540886 1.2931183112620578e-89
VAE0 H3K27ac 0.565460701897515 1.1067285645987936e-116
VAE0 H3K36me3 0.5128226801227292 8.11907865775757e-93
VAE0 H3K27me3 0.03803275569023637 0.15928977898633345
VAE0 Log2FC FB -0.3565934229558133 2.2360651454584692e-42
VAE0 Log2FC MB -0.41092267559995416 5.448112954456422e-57
VAE0 Log2FC HB -0.33584075926758683 1.696244835667772e-37
VAE0 Log2FC SC -0.30086852336773884 4.42036845192071e-30
VAE0 VAE0 1.0 0.0
VAE0 VAE1 -0.044440792633740046 0.10000658140919086
VAE0 VAE2 -0.06607105313289889 0.014410819464438108
VAE1 H3K4me1 -0.42943166638024277 1.2627522245545507e-62
VAE1 H3K4me2 -0.3459967816690149 7.715291848745455e-40
VAE1 H3K4me3 -0.29554922126419003 4.8781949636658434e-29
VAE1 H3K27ac -0.13974158340089593 2.0471583971385752e-07
VAE1 H3K36me3 0.0469662393901308 0.08214161188440466
VAE1 H3K27me3 -0.9347886653056977 0.0
VAE1 Log2FC FB -0.4254659838623871 2.180071569852324e-61
VAE1 Log2FC MB -0.23624776872255862 7.626021772616817e-19
VAE1 Log2FC HB 0.0008937253954734859 0.9736252988078797
VAE1 Log2FC SC -0.19659919987030305 2.0632386010018648e-13
VAE1 VAE0 -0.044440792633740046 0.10000658140919086
VAE1 VAE1 1.0 0.0
VAE1 VAE2 -0.010831726378816332 0.6886318434581902
VAE2 H3K4me1 0.1608669464307792 2.097868335239766e-09
VAE2 H3K4me2 0.0547856481985845 0.042537576997147844
VAE2 H3K4me3 0.08246692173925642 0.0022436715756004757
VAE2 H3K27ac 0.02811476348996577 0.29821822708243745
VAE2 H3K36me3 0.016248697834837858 0.5477522547037476
VAE2 H3K27me3 0.06358119033023919 0.01854979231058591
VAE2 Log2FC FB -0.2673799783130146 7.108362262493199e-24
VAE2 Log2FC MB -0.02289057494832727 0.39704663424441755
VAE2 Log2FC HB 0.16871385420091436 3.2493899832182813e-10
VAE2 Log2FC SC 0.0544089035764286 0.043982879996444985
VAE2 VAE0 -0.06607105313289889 0.014410819464438108
VAE2 VAE1 -0.010831726378816332 0.6886318434581902
VAE2 VAE2 1.0 0.0
<Figure size 216x216 with 0 Axes>
  external_gene_name  wt11fb_merged-rep  wt13fb_merged-rep  wt15fb_merged-rep  \
0               Pax2           2.002935           0.805872           0.687880   
1              Hoxb9           0.479086           0.220758           0.272247   
2              Hoxc9           0.070489           0.020139           0.114239   
3              Hoxd9           0.018483           0.058725           0.189060   

   wt18fb_merged-rep  wt11mb_merged-rep  wt13mb_merged-rep  wt15mb_merged-rep  \
0           0.319063           2.417627           3.253193           3.635322   
1           0.172209           0.316455           0.084808           0.145274   
2           0.111092           0.071978           0.022155           0.032423   
3           0.075711           0.090560           0.000000           0.148719   

   wt18mb_merged-rep  wt11hb_merged-rep  wt13hb_merged-rep  wt15hb_merged-rep  \
0           3.593899           5.158974           7.442453           6.735870   
1           0.222129           0.583028           1.544333           2.252819   
2           0.016071           0.118288           0.018302           0.081508   
3           0.031184           0.035835           0.071966           0.072328   

   wt18hb_merged-rep  wt11sc_merged-rep  wt13sc_merged-rep  wt15sc_merged-rep  \
0           6.124097           4.730448           7.405267           6.410930   
1           3.759467           8.786930           9.736304           8.394440   
2           1.539684           5.871115           6.350094           5.333934   
3           0.048047           5.252176           3.308305           5.315772   

   wt18sc_merged-rep  
0           6.355501  
1           8.328319  
2           5.571739  
3           5.567879  
  external_gene_name  ko11fb_merged-rep  ko13fb_merged-rep  ko15fb_merged-rep  \
0               Pax2           3.108827           5.765663           5.404836   
1              Hoxb9           1.027615           3.872195           2.840668   
2              Hoxc9           1.703283           6.086356           5.200582   
3              Hoxd9           1.600412           5.909166           4.968913   

   ko18fb_merged-rep  ko11mb_merged-rep  ko13mb_merged-rep  ko15mb_merged-rep  \
0           4.963471           3.909758           6.262265           5.467721   
1           3.095592           2.442091           4.576879           2.964123   
2           4.578695           2.760883           5.691054           4.920412   
3           4.652042           2.412880           6.038424           4.916205   

   ko18mb_merged-rep  ko11hb_merged-rep  ko13hb_merged-rep  ko15hb_merged-rep  \
0           4.525031           4.210750           6.481735           5.950515   
1           2.827947           2.375786           6.178522           3.968796   
2           4.175629           2.662632           5.024299           4.750092   
3           4.574977           2.258272           5.722458           5.016718   

   ko18hb_merged-rep  ko11sc_merged-rep  ko13sc_merged-rep  ko15sc_merged-rep  \
0           5.205509           4.984483           6.903416           6.332971   
1           4.295223           9.149745           8.563543           7.179321   
2           4.168916           6.278640           5.842700           5.185208   
3           4.585869           5.649826           6.279059           5.465261   

   ko18sc_merged-rep  
0           5.704444  
1           6.969096  
2           5.005286  
3           5.319954  
  external_gene_name  wt11fb_merged-rep  wt13fb_merged-rep  wt15fb_merged-rep  \
0               Otx2           5.758462           5.531050           4.405259   
1                En2           0.494674           0.168652           0.353709   
2             Phox2b           0.102214           0.132168           0.119700   
3              Hoxd3           0.164494           0.133004           0.149337   

   wt18fb_merged-rep  wt11mb_merged-rep  wt13mb_merged-rep  wt15mb_merged-rep  \
0           3.893296           7.722552           7.356560           6.650007   
1           0.413756           7.499362           8.031306           7.013888   
2           0.015052           2.805449           3.955419           3.151693   
3           0.111092           0.140517           0.175505           1.068782   

   wt18mb_merged-rep  wt11hb_merged-rep  wt13hb_merged-rep  wt15hb_merged-rep  \
0           5.799004           3.708020           1.019738           0.976341   
1           6.668011           6.872836           1.465594           0.375820   
2           2.330060           6.469630           7.075044           6.013573   
3           0.123280           5.589150           8.062120           7.073398   

   wt18hb_merged-rep  wt11sc_merged-rep  wt13sc_merged-rep  wt15sc_merged-rep  \
0           4.221625           0.527910           0.036119           0.063529   
1           0.983470           0.548630           0.247523           0.205953   
2           5.348831           3.407077           1.318731           0.177374   
3           6.354484           6.996139           7.274181           5.681410   

   wt18sc_merged-rep  
0           0.079962  
1           0.233611  
2           0.032084  
3           5.317846  
  external_gene_name  ko11fb_merged-rep  ko13fb_merged-rep  ko15fb_merged-rep  \
0               Otx2           7.054284           5.931086           5.888406   
1                En2           4.821768           5.241948           4.639493   
2             Phox2b           1.599360           1.065941           0.572130   
3              Hoxd3           1.231702           5.185332           4.399199   

   ko18fb_merged-rep  ko11mb_merged-rep  ko13mb_merged-rep  ko15mb_merged-rep  \
0           5.319730           5.748901           5.884490           5.933551   
1           4.450940           6.970253           7.608890           6.280874   
2           0.214009           4.499223           3.635362           1.911110   
3           3.833791           3.028335           5.972110           4.999986   

   ko18mb_merged-rep  ko11hb_merged-rep  ko13hb_merged-rep  ko15hb_merged-rep  \
0           5.312715           5.090304           4.275430           4.738128   
1           6.268489           4.803762           5.565673           6.119783   
2           2.076850           3.455354           5.766164           4.587203   
3           3.755855           3.310889           5.867555           5.133170   

   ko18hb_merged-rep  ko11sc_merged-rep  ko13sc_merged-rep  ko15sc_merged-rep  \
0           3.662862           0.820287           2.904100           2.846001   
1           4.860775           3.372272           5.201905           4.665178   
2           5.165144           2.972264           1.325385           1.776973   
3           4.737131           7.106383           6.408114           5.623793   

   ko18sc_merged-rep  
0           2.294013  
1           4.312455  
2           0.466172  
3           5.027903  
Out[11]:
<Axes3DSubplot:xlabel='Node 1', ylabel='Node 2'>
<Figure size 360x360 with 0 Axes>
In [12]:
""" Print out the gene lists. """

gene_markers_sep = [['Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6'], 
                    ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4'], 
                    ['Hoxb1', 'Krox20', 'Fev', 'Hoxd3', 'Phox2b'],
                    ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11', 
                    'Hoxa13', 'Hoxb9', 'Hoxb13',  'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13'],
                    ['Ccna1', 'Ccna2', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Cdc25a', 
                    'Cdc25b', 'Cdc25c', 'E2f1', 'E2f2', 'E2f3', 'Mcm10', 'Mcm5', 'Mcm3', 'Mcm2', 'Cip2a'],
                    ['Cdkn1a', 'Cdkn1b', 'Cdkn1c', 'Cdkn2a', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d'],
                    ['Sox2', 'Sox1', 'Sox3', 'Hes1', 'Hes5'],
                    ['Snap25', 'Syt1', 'Slc32a1','Slc17a6', 'Syn1'],
                    ['Cspg4', 'Aqp4', 'Slc6a11', 'Olig1', 'Igfbp3'],
                    ['Foxg1'], 
                    ['En2'], 
                    ['Phox2b'],
                    ['Hoxc9'],
                    ['Sox3']
                    ]

marker_labels_sep = ['forebrain', 'midbrain', 'hindbrain',  'spinalcord', 
                     'Proliferation', 'Negative regulators of Cell Cycle', 
                    'progenitors', 'neurons', 'glia',
                     'Foxg1', 'En2', 'Phox2b', 'Hoxc9', 'Sox3'
                    ]
# Print genes that were actually in our dataset 
cx = [g for g in df[gene_name].values if g in ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11', 
                    'Hoxa13', 'Hoxb9', 'Hoxb13',  'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13']]
print(', '.join(cx))

len(cx)
cx = [g for g in df[gene_name].values if g in ['Sox2', 'Sox1', 'Sox3', 'Hes1', 'Hes5']]
print(', '.join(cx))
cx = [g for g in df[gene_name].values if g in ['Snap25', 'Syt1', 'Slc32a1','Slc17a6', 'Syn1']]
print(', '.join(cx))
cx = [g for g in df[gene_name].values if g in['Cspg4', 'Aqp4', 'Slc6a11', 'Olig1', 'Igfbp3']]
print(', '.join(cx))
prolif = ['Ccna1', 'Ccna2', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Cdc25a', 'Cdc25b', 'Cdc25c', 'E2f1', 'E2f2', 'E2f3', 'Mcm10', 'Mcm5', 'Mcm3', 'Mcm2', 'Cip2a']
anti_prolif = ['Cdkn1a', 'Cdkn1b', 'Cdkn1c', 'Cdkn2a', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d']
pl = [g for g in df[gene_name].values if g in prolif]
apl = [g for g in df[gene_name].values if g in anti_prolif]
print(', '.join(pl))
print(', '.join(apl))
Hoxb13, Hoxb9, Hoxc13, Hoxc12, Hoxc11, Hoxc10, Hoxc9, Hoxc8, Hoxd13, Hoxd11, Hoxd10, Hoxd9, Hoxd8, Hoxa7, Hoxa9, Hoxa10, Hoxa11, Hoxa13
Hes5, Sox3
Snap25
Aqp4
Cip2a, Cdc25c, Mcm10, E2f1, Ccna2, Ccnd1
Cdkn1a, Cdkn2a, Cdkn2b

5) Quantify separability of latent space

In the next section we compare the distances in the latent space to those using other summaries of the data.

The distances are computed as the euclidean distance to the centroid. Where the centroid is either the center of the cluster of genes of interest OR the center of the two groups. We expect the referece to have a greater spread and that our genes should be closer to the centroid of the gene set they belong to.

Datasets tested: 1) VAE deep (3 layer) 2) VAE shallow (1 layer) 3) PCA (3 PCs) 4) RNAseq data (normalised TMM 64 features) 5) VAE input data (normalised TMM and normalised H3K27me3 and LogFC, 100 features) 6) logFC forebrain and H3K27me3 signal.

In [13]:
###############################################################################
#                                                                             #
#    This program is free software: you can redistribute it and/or modify     #
#    it under the terms of the GNU General Public License as published by     #
#    the Free Software Foundation, either version 3 of the License, or        #
#    (at your option) any later version.                                      #
#                                                                             #
#    This program is distributed in the hope that it will be useful,          #
#    but WITHOUT ANY WARRANTY; without even the implied warranty of           #
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the            #
#    GNU General Public License for more details.                             #
#                                                                             #
#    You should have received a copy of the GNU General Public License        #
#    along with this program. If not, see <http://www.gnu.org/licenses/>.     #
#                                                                             #
###############################################################################
from collections import defaultdict
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from statannot import add_stat_annotation

from sciviso import Vis


class Boxplot(Vis):
    """
    Box plot. Adds stat annotations and returns the SVG or saves it to disk.
    for stats annotations details see: https://github.com/webermarcolivier/statannot
    """
    def __init__(self, df: pd.DataFrame, x: object, y: object, title='', xlabel='', ylabel='', box_colors=None,
                 hue=None, order=None, hue_order=None, showfliers=False, add_dots=False, add_stats=True,
                 stat_method='Mann-Whitney', box_pairs=None, figsize=(1.5, 1.5), title_font_size=8, label_font_size=6, title_font_weight=700):
        super().__init__(df, figsize=figsize, title_font_size=title_font_size, label_font_size=label_font_size,
                         title_font_weight=title_font_weight)
        self.df = df
        self.x = x
        self.y = y
        self.title = title
        self.hue = hue
        self.order = order
        self.hue_order = hue_order
        self.showfliers = showfliers
        self.add_dots = add_dots
        self.add_stats = add_stats
        self.stat_method = stat_method
        self.box_pairs = box_pairs
        self.label = 'boxplot'
        self.xlabel = xlabel
        self.ylabel = ylabel
        self.box_colors = box_colors

    def format_data_for_boxplot(self, df: pd.DataFrame, conditions: list, filter_column=None, filter_values=None):
        condition_dict = defaultdict(list)
        for column in df.columns:
            for c in conditions:
                if c in column:
                    condition_dict[c].append(column)

        # Now lets get the values
        values = []
        condition = []
        samples = []
        if filter_column is None or filter_values is None:
            for i in range(0, len(df)):
                for cond, columns in condition_dict.items():
                    for c in columns:
                        values.append(df[c].values[i])
                        condition.append(cond)
                        samples.append(c)
        else:
            i = 0
            for v in df[filter_column].values:
                if v in filter_values:
                    for cond, columns in condition_dict.items():
                        for c in columns:
                            values.append(df[c].values[i])
                            condition.append(cond)
                            samples.append(c)
                i += 1
        box_df = pd.DataFrame()
        box_df['Samples'] = samples
        box_df['Values'] = values
        box_df['Conditions'] = condition
        return box_df

    def plot(self, legend=True):
        x, y, hue_order, order, hue, box_pairs = self.x, self.y, self.hue_order, self.order, self.hue, self.box_pairs
        # First lets check whether we were passed lists or strings for our y and x arrays
        if not isinstance(x, str) and not isinstance(y, str):
            vis_df = pd.DataFrame()
            vis_df['x'] = x
            vis_df['y'] = y
            x = 'x'
            y = 'y'
            if hue is not None:
                vis_df['colour'] = hue
                hue = 'colour'
            if order is None:
                order = list(set(vis_df['x'].values))
                order.sort()
        else:
            vis_df = self.df

        # set the orders
        if hue_order is None and hue is not None:
            hue_order = list(set(vis_df[hue].values))
            hue_order.sort()
        if order is None:
            order = list(set(vis_df[x].values))
            order.sort()

        ax = sns.boxplot(data=vis_df, x=x, y=y, hue=hue, hue_order=hue_order, order=order, palette=self.palette,
                         showfliers=self.showfliers)
        if self.add_dots:
            ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order, order=order, alpha=0.5, color='.2')
        if self.add_stats:
            # Add all pairs in the order if the box pairs is none
            if box_pairs is None:
                pairs = []
                box_pairs = []
                for i in order:
                    for j in order:
                        if i != j:
                            # Ensure we don't get duplicates
                            pair = f'{i}{j}' if i < j else f'{j}{i}'
                            if pair not in pairs:
                                box_pairs.append((i, j))
                                pairs.append(pair)
            # Add stats annotation
            add_stat_annotation(ax, data=vis_df, x=x, y=y, order=order,
                                box_pairs=box_pairs,
                                test=self.stat_method, text_format='star', loc='inside', verbose=2)

        ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right', weight = 'bold')
        if legend:
            plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.,  fontsize=self.label_font_size)
        ax.tick_params(labelsize=self.label_font_size)
        self.add_labels()

        # Check if the user supplied a list of colours for the boxes:
        if self.box_colors is not None:
            for i, b in enumerate(ax.artists):
                b.set_facecolor(self.box_colors[i])
        self.set_ax_params(ax)
        return ax

6) Save ranks for GSEA

We perform GSEA on the ranks of the VAE.

In [15]:
# -----------------------------------------------------------------------------------
#                         Save genes sorted by latent node (GSEA)
# -----------------------------------------------------------------------------------
for n in range(0, num_nodes):
    with open(f'{ora_dir}vae{n}_genes_{experiment_name}_{date}.csv', 'w+') as f:
        f.write(gene_id + ',value\n')
        desc_sorted = (-vae_data[:,n]).argsort()  # Sort the genes by descending order
        genes = df[gene_id].values[desc_sorted]
        vae_data_sorted = vae_data[:,n][desc_sorted]
        i = 0
        for g in genes:
            f.write(f'{g},{vae_data_sorted[i]}\n')
            i += 1
            

# -----------------------------------------------------------------------------------
#                         Save genes sorted by latent node (GSEA)
# -----------------------------------------------------------------------------------
for n in range(0, num_nodes):
    with open(f'{ora_dir}vae{n}_gene-names_{experiment_name}_{date}.csv', 'w+') as f:
        f.write(gene_name + ',value\n')
        desc_sorted = (-vae_data[:,n]).argsort()  # Sort the genes by descending order
        genes = df[gene_name].values[desc_sorted]
        vae_data_sorted = vae_data[:,n][desc_sorted]
        i = 0
        for g in genes:
            f.write(f'{g},{vae_data_sorted[i]}\n')
            i += 1

7) Compute clusters driving VAE

In the next section, we use information about the VAE nodes to compute clusters regarding the latent space.

In [16]:
def get_q1_q3(node_idx):
    iqr_val = 0.95 * stats.iqr(vae_data[:, node_idx])
    q1_genes_idxs = np.where(vae_data[:, node_idx] <= -1.25)[0] # (np.mean(vae_data[:, node_idx]) - iqr_val))[0]
    q3_genes_idxs = np.where(vae_data[:, node_idx] >= 1.25)[0] #(np.mean(vae_data[:, node_idx]) + iqr_val))[0]
    return q1_genes_idxs, q3_genes_idxs

# -----------------------------------------------------------------------------------
#                         Find the genes driving the latent space
# -----------------------------------------------------------------------------------

n_clusters = 6
# Get the indexs for each of the q1 and q3 for each node
q1_n0_idxs, q3_n0_idxs = get_q1_q3(0)
q1_n1_idxs, q3_n1_idxs = get_q1_q3(1)
q1_n2_idxs, q3_n2_idxs = get_q1_q3(2)

# Create a list of the indexs
vae_set_idxs = [
    q1_n0_idxs, q3_n0_idxs, 
    q1_n1_idxs, q3_n1_idxs, 
    q1_n2_idxs, q3_n2_idxs
]

vae_set_labels = ['Set 1', 'Set 2', 'Set 3', 'Set 4', 'Set 5', 'Set 6']

# Get all the driver and non-driver idxs
non_driver_idxs = []
driver_idxs = []
for i, ids in enumerate(vae_set_idxs):
    vae_mse.u.dp([vae_set_labels[i], len(ids)])
    driver_idxs += list(ids)
    
for i in range(0, len(df)):
    if i not in driver_idxs:
        non_driver_idxs.append(i)
# -----------------------------------------------------------------------------------
#                         Plot a VENN of how the genes in teh clusters overlap
# -----------------------------------------------------------------------------------

gene_sets = [[], [], [], [], [], []]

for i, g in enumerate(df[gene_name].values):
    ci = 0
    for gene_set in vae_set_idxs:
        for j in gene_set:
            if i == j:
                gene_sets[ci].append(g)
        ci += 1

set_sets = []
for g in gene_sets:  
    if len(g) > 1:
        set_sets.append(set(g))

set_labels = venn.get_labels(set_sets)
vae_set_labels_l = [s.replace('Set', 'Group') for s in vae_set_labels]
vae_set_genes_len_dict = {}
vae_set_genes_dict = {}
for i, l in enumerate(vae_set_labels):
    vae_set_genes_dict[l] = gene_sets[i]
    vae_set_genes_len_dict[l] = len(gene_sets[i])

venn.venn6(set_labels, names=vae_set_labels_l)
save_fig('gene_sets.svg')
plt.show()


vae_set_genes_as_list = []
for set_name in vae_set_labels:
    vae_set_genes_as_list.append(vae_set_genes_dict.get(set_name))

sorted_sets_by_size = {k: v for k, v in sorted(vae_set_genes_len_dict.items(), key=lambda item: item[1])}
gene_set_labels_unique = []
for g in df[gene_name].values:
    found = False
    for label, values in sorted_sets_by_size.items():
        gene_group = vae_set_genes_dict[label]
        for g_l in gene_group:
            if g_l == g:
                gene_set_labels_unique.append(label)
                found = True
                break
        if found:
            break
    if not found:
        gene_set_labels_unique.append(None)
df['GeneDriverSet'] =  gene_set_labels_unique

# We also want to add each of the gene sets
gene_labels = [[], [], [], [], [], []]
for g in df[gene_name].values:
    for i, label in enumerate(vae_set_labels):
        found = False
        gene_group = vae_set_genes_dict[label]
        for g_l in gene_group:
            if g == g_l:
                gene_labels[i].append(True)
                found = True
                break
        if not found:
            gene_labels[i].append(False)
for i, label in enumerate(vae_set_labels):
    df[label] = gene_labels[i]
    
            
# -----------------------------------------------------------------------------------
#                         Plot the genes on a scatter plot
# -----------------------------------------------------------------------------------
vae_vis.plot_values_on_scatters(df, gene_name, vae_set_labels,
                            vae_set_genes_as_list, vae_data=vae_data, output_dir=fig_dir, plt_bg=True,
                            title=f'VAE gene driver sets {experiment_name}',
                            show_plt=True, save_fig=True)

# -----------------------------------------------------------------------------------
#                         Plot histograms of the VAE nodes with cutoffs used above
# -----------------------------------------------------------------------------------
for n in range(0, num_nodes):
    x = vae_data[:,n]
    iqr_val = 1.0 * stats.iqr(x)
    plt.hist(vae_data[:,n], bins=20, color='grey')
    plt.ylim(0, 380)
    plt.axvline(x.mean() - iqr_val, color='k', linestyle='dashed', linewidth=1)
    plt.axvline(x.mean() + iqr_val, color='r', linestyle='dashed', linewidth=1)
    save_fig(f'hist-{n}')
    plt.show()
--------------------------------------------------------------------------------
                                   Set 1	62	                                    
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
                                   Set 2	180	                                   
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
                                   Set 3	80	                                    
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
                                   Set 4	223	                                   
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
                                   Set 5	156	                                   
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
                                   Set 6	121	                                   
--------------------------------------------------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/venn/_backwards_compatibility.py:15: UserWarning: `get_labels()` is retained for backwards compatibility; use `generate_petal_labels()` or the higher level `venn()` instead
  warn((
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/venn/_backwards_compatibility.py:30: UserWarning: `venn6()` is retained for backwards compatibility; use `venn()` instead
  warn((
<Figure size 72x72 with 0 Axes>
In [17]:
hox_genes = sc_genes
forebrain_genes = fb_genes
h3cols = []
fb_rna_cols = []
sc_rna_cols = []

# Do it in this way so they are correctly ordered
for c in df.columns:
    if 'H3K27me' in c and 'signal' in c:
        h3cols.append(c)
    elif 'wt' in c:
        if 'fb' in c:
            fb_rna_cols.append(c)
        if 'sc' in c:
            sc_rna_cols.append(c)
for c in df.columns:
    if 'ko' in c:
        if 'fb' in c:
            fb_rna_cols.append(c)
        if 'sc' in c:
            sc_rna_cols.append(c)

def plot_gene_boxplot(df, title, cluster_id, cols, idxs):
    idxs = idxs[cluster_id]
    boxplot = Boxplot(df, gene_name, cols[0])
    box_df = boxplot.format_data_for_boxplot(df, cols, gene_name, df[gene_name].values[idxs])
    is_hox = []
    sns.set_style("white")
    for c in box_df['Samples'].values:
        if 'Hox' in c:
            is_hox.append('Hox')
        else:
            is_hox.append('Hox like')
    box_df['is_hox'] = is_hox

    boxplot = Boxplot(box_df, "Conditions", "Values", add_stats=False, add_dots=False, figsize=(1,0.5),
                       order=cols)
    boxplot.palette = sci_colour

    ax = boxplot.plot()
    plt.title(title)
    ax.set_ylim(0, 12)
    sns.set_style("white")
    c = 0
    for b in ax.artists:
        if c < len(cols)/2:
            b.set_facecolor(wt_colour)
        else:
            b.set_facecolor(ko_colour)
        c += 1
    save_fig(f'{title}')
    
    plt.show()

avgs_fb = [['wt11fb1',
         'wt11fb2'],
         ['wt13fb1',
         'wt13fb2'],
         ['wt15fb1',
         'wt15fb2'],
         ['wt18fb1',
         'wt18fb2'],
         ['ko11fb1',
         'ko11fb2'],
         ['ko13fb1',
         'ko13fb2'],
         ['ko15fb1',
         'ko15fb2'],
         ['ko18fb1',
         'ko18fb2']]

avgs_sc = [['wt11sc1',
         'wt11sc2'],
         ['wt13sc1',
         'wt13sc2'],
         ['wt15sc1',
         'wt15sc2'],
         ['wt18sc1',
         'wt18sc2'],
         ['ko11sc1',
         'ko11sc2'],
         ['ko13sc1',
         'ko13sc2'],
         ['ko15sc1',
         'ko15sc2'],
         ['ko18sc1',
         'ko18sc2']]

avgs_hb = [['wt11hb1',
         'wt11hb2'],
         ['wt13hb1',
         'wt13hb2'],
         ['wt15hb1',
         'wt15hb2'],
         ['wt18hb1',
         'wt18hb2'],
         ['ko11hb1',
         'ko11hb2'],
         ['ko13hb1',
         'ko13hb2'],
         ['ko15hb1',
         'ko15hb2'],
         ['ko18hb1',
         'ko18hb2']]

avgs_mb = [['wt11mb1',
         'wt11mb2'],
         ['wt13mb1',
         'wt13mb2'],
         ['wt15mb1',
         'wt15mb2'],
         ['wt18mb1',
         'wt18mb2'],
         ['ko11mb1',
         'ko11mb2'],
         ['ko13mb1',
         'ko13mb2'],
         ['ko15mb1',
         'ko15mb2'],
         ['ko18mb1',
         'ko18mb2']]
avgs_df = pd.DataFrame()
avgs_df[gene_name] = df[gene_name].values
fb_cols = []
for f in avgs_fb:
    new_col = f'{f[0][:2]} {f[0][2:4]} FB'
    avgs_df[new_col] = (df[f[0]].values + df[f[1]].values) / 2.0
    fb_cols.append(new_col)
    
sc_cols = []
for f in avgs_sc:
    new_col = f'{f[0][:2]} {f[0][2:4]} SC'
    avgs_df[new_col] = (df[f[0]].values + df[f[1]].values) / 2.0
    sc_cols.append(new_col)
    
mb_cols = []
for f in avgs_mb:
    new_col = f'{f[0][:2]} {f[0][2:4]} MB'
    avgs_df[new_col] = (df[f[0]].values + df[f[1]].values) / 2.0
    mb_cols.append(new_col)
    
hb_cols = []
for f in avgs_hb:
    new_col = f'{f[0][:2]} {f[0][2:4]} HB'
    avgs_df[new_col] = (df[f[0]].values + df[f[1]].values) / 2.0
    hb_cols.append(new_col)
    
for i, gs in enumerate(vae_set_idxs):
    plot_gene_boxplot(avgs_df, f'Cluster {i + 1} FB', i, fb_cols, vae_set_idxs)
    plot_gene_boxplot(avgs_df, f'Cluster {i + 1} MB', i, mb_cols, vae_set_idxs)
    plot_gene_boxplot(avgs_df, f'Cluster {i + 1} HB', i, hb_cols, vae_set_idxs)
    plot_gene_boxplot(avgs_df, f'Cluster {i + 1} SC', i, sc_cols, vae_set_idxs)
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.

Plot the heatmaps

In [18]:
def plot_vae_rna_cols(vis_df, cols, num_nodes, fb_logfc, mb_logfc, hb_logfc, sc_logfc, 
                      h3k27me3_fb_e11, h3k27me3_fb_e18, h3k27me3_hb_e11, h3k27me3_hb_e18, title='Heatmap', 
                      num_values=10):
    vae_cols = [f'VAE{i}' for i in range(0, num_nodes)]

    sns.set_style("ticks")
    for r_i, r_c in enumerate(cols):
        for direction in ['up', 'down']:
            # First add the bottom ones
            fig, ax = plt.subplots()
            sns.set(rc={'figure.figsize': (3.5, 2), 'font.family': 'sans-serif',
            'font.sans-serif': 'Arial', 'font.size': 6}, style="ticks")
            rcm_df_sorted = vis_df.sort_values(by=[r_c])
            if direction == 'up':
                rcm_df_tails = rcm_df_sorted.nlargest(num_values, r_c)
            else:
                rcm_df_tails = rcm_df_sorted.nsmallest(num_values, r_c)
            # Add the smallest and the largest
            # first heatmap
            N = len(rcm_df_tails)
            ax.set_xlim(-0.5, 7.5)
            ax.set_xticks([0, 1, 2, 3, 4, 5, 6, 7])
            ax.set_xticklabels(['FB', 'MB', 'HB', 'SC', 
                                'FB e11.5', 'FB e18.5', 'HB e11.5', 'HB e18'])
            ax.set_yticks(range(N))
            ax.set_yticklabels(list(rcm_df_tails[gene_name].values))
            ax.set_ylabel('Genes')

            max_hk3 = 35
            max_rna = 10
            min_rna = -10
            cmap_rna = 'seismic'
            cmap_hk3 = 'Greens'
            im1 = ax.imshow(np.vstack([rcm_df_tails[fb_logfc], rcm_df_tails[fb_logfc]]).T, 
                            aspect='auto',
                            extent=[-0.5, 0.5, -0.5, N - 0.5], vmin=min_rna, vmax=max_rna,
                            origin='lower', cmap=cmap_rna)
            im1 = ax.imshow(np.vstack([rcm_df_tails[mb_logfc], rcm_df_tails[mb_logfc]]).T, 
                            aspect='auto',
                            extent=[0.5, 1.5, -0.5, N - 0.5], vmin=min_rna, vmax=max_rna,
                            origin='lower', cmap=cmap_rna)
            im1 = ax.imshow(np.vstack([rcm_df_tails[hb_logfc], rcm_df_tails[hb_logfc]]).T, 
                aspect='auto',
                extent=[1.5, 2.5, -0.5, N - 0.5], vmin=min_rna, vmax=max_rna,
                origin='lower', cmap=cmap_rna)
            # Now add in the H3K27me3 ones
            im1 = ax.imshow(np.vstack([rcm_df_tails[sc_logfc], rcm_df_tails[sc_logfc]]).T, 
                aspect='auto',
                extent=[2.5, 3.5, -0.5, N - 0.5], vmin=min_rna, vmax=max_rna,
                origin='lower', cmap=cmap_rna)

            im3 = ax.imshow(np.vstack([rcm_df_tails[h3k27me3_fb_e11], rcm_df_tails[h3k27me3_fb_e11]]).T, 
                            aspect='auto',
                            extent=[3.5, 4.5, -0.5, N - 0.5], vmin=0, vmax=max_hk3,
                            origin='lower', cmap=cmap_hk3)

            im3 = ax.imshow(np.vstack([rcm_df_tails[h3k27me3_fb_e18], rcm_df_tails[h3k27me3_fb_e18]]).T, 
                aspect='auto',
                extent=[4.5, 5.5, -0.5, N - 0.5], vmin=0, vmax=max_hk3,
                origin='lower', cmap=cmap_hk3)

            im3 = ax.imshow(np.vstack([rcm_df_tails[h3k27me3_hb_e11], rcm_df_tails[h3k27me3_hb_e11]]).T, 
                            aspect='auto',
                            extent=[5.5, 6.5, -0.5, N - 0.5], vmin=0, vmax=max_hk3,
                            origin='lower', cmap=cmap_hk3)

            im3 = ax.imshow(np.vstack([rcm_df_tails[h3k27me3_hb_e18], rcm_df_tails[h3k27me3_hb_e18]]).T, 
                aspect='auto',
                extent=[6.5, 7.5, -0.5, N - 0.5], vmin=0, vmax=max_hk3,
                origin='lower', cmap=cmap_hk3)

            cbar1 = fig.colorbar(im1, ax=ax, label='RNA logFC')
            cbar2 = fig.colorbar(im3, ax=ax, label='H3K27me3 signal')

            fig.tight_layout()
            plt.xticks(rotation=45, horizontalalignment='right')
            plt.title(f'{title} {r_c}')
            save_fig(f'{title.replace(" ", "_")}_{direction}_{r_c}')
In [19]:
h3k27me3_fb_e11 = [c for c in df.columns if '10.5' in c and 'forebrain' in c and 'H3K27me3' in c and 'signal' in c and 'median' not in c][0]
h3k27me3_fb_e18 = [c for c in df.columns if '16.5' in c and 'forebrain' in c and 'H3K27me3' in c and 'signal' in c and 'median' not in c][0]

h3k27me3_hb_e11 = [c for c in df.columns if '10.5' in c and 'hindbrain' in c and 'H3K27me3' in c and 'signal' in c and 'median' not in c][0]
h3k27me3_hb_e18 = [c for c in df.columns if '16.5' in c and 'hindbrain' in c and 'H3K27me3' in c and 'signal' in c and 'median' not in c][0]

plot_vae_rna_cols(df, ["VAE0", "VAE1", "VAE2"], 3,     
                    'Log2FC FB',
                    'Log2FC MB',
                    'Log2FC HB',
                    'Log2FC SC', 
                     h3k27me3_fb_e11, h3k27me3_fb_e18, h3k27me3_hb_e11, h3k27me3_hb_e18)
<ipython-input-18-741904c72e35>:75: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. 
  fig.tight_layout()

Save the VAE groups for ORA

Here we save the groups for ORA and the ranks for GSEA.

In [20]:
# -----------------------------------------------------------------------------------
#                         Save background genes (ORA)
# -----------------------------------------------------------------------------------

with open(f'{ora_dir}background_genes_{experiment_name}_{date}.csv', 'w+') as f:
    genes = df_all[gene_id].values
    f.write(gene_id + '\n')
    for g in genes:
        f.write(f'{g}\n')
        
# -----------------------------------------------------------------------------------
#                         Save training genes (sig genes) (ORA)
# -----------------------------------------------------------------------------------
with open(f'{ora_dir}training-df_genes_{experiment_name}_{date}.csv', 'w+') as f:
    genes = df_training[gene_id].values
    f.write(gene_id + '\n')
    for g in genes:
        f.write(f'{g}\n')
        
# -----------------------------------------------------------------------------------
#                         Save genes in each of the clusters we annotated (ORA)
# -----------------------------------------------------------------------------------
for i in range(0, n_clusters):
    with open(f'{ora_dir}cluster_{i}_genes_{experiment_name}_{date}.csv', 'w+') as f:
        genes = df[gene_id].values[vae_set_idxs[i]]
        f.write(gene_id + '\n')
        for g in genes:
            f.write(f'{g}\n')
    
# -----------------------------------------------------------------------------------
#                         Save genes sorted by latent node (GSEA) and only the top
# -----------------------------------------------------------------------------------
def plot_top_vae_genes(vae_data_sorted, gene_ids_sorted, gene_names_sorted, label="", num=20):
        i = 0
        gs = []
        for g in gene_names_sorted:
            if g != "0":
                f.write(f'{g},{vae_data_sorted[i]}\n')
                gs.append(g)
            else:
                f.write(f'{gene_ids_sorted[i]},{vae_data_sorted[i]}\n')
                gs.append(gene_ids_sorted[i])
            i += 1
            if i >= num:
                break
        heatmap_df = pd.DataFrame()
        heatmap_df[gene_name] = gs
        heatmap_df['values'] = vae_data_sorted[:num]
        heatmap = Heatmap(heatmap_df, ['values'], gene_name, vmin=-4, vmax=4,
                  title=f'top {num} genes', cluster_cols=False, cluster_rows=False)
        heatmap.palette=sns.color_palette(sci_colour)
        heatmap.plot()
        pplot()
        save_fig(f'vae{n}_genes-top-{num}-{label}')
        plt.show()
        
for n in range(0, num_nodes):
    with open(f'{ora_dir}vae-{n + 1}_genes-top10_{experiment_name}_{date}.csv', 'w+') as f:
        f.write(gene_id + ',value\n')
        desc_sorted = (-vae_data[:,n]).argsort()  # Sort the genes by descending order
        gene_names_sorted = df[gene_name].values[desc_sorted]
        gene_ids_sorted = df[gene_id].values[desc_sorted]
        vae_data_sorted = vae_data[:,n][desc_sorted]
        plot_top_vae_genes(vae_data_sorted, gene_ids_sorted, gene_names_sorted, label="top")
        
    with open(f'{ora_dir}vae-{n + 1}_genes-bottom10_{experiment_name}_{date}.csv', 'w+') as f:
        f.write(gene_id + ',value\n')
        desc_sorted = (vae_data[:,n]).argsort()  # Sort the genes by descending order
        gene_names_sorted = df[gene_name].values[desc_sorted]
        vae_data_sorted = vae_data[:,n][desc_sorted]
        gene_ids_sorted = df[gene_id].values[desc_sorted]
        plot_top_vae_genes(vae_data_sorted, gene_ids_sorted, gene_names_sorted, label="bottom")

for n in range(0, num_nodes):
    with open(f'{ora_dir}vae-{n + 1}_{experiment_name}_{date}.csv', 'w+') as f:
        f.write(gene_id + ',value\n')
        desc_sorted = (vae_data[:,n]).argsort()  # Sort the genes by descending order
        gene_names_sorted = df[gene_id].values[desc_sorted]
        vae_data_sorted = vae_data[:,n][desc_sorted]
        for i, g in enumerate(gene_names_sorted):
            f.write(f'{g},{vae_data_sorted[i]}\n')
In [21]:
# -----------------------------------------------------------------------------------
#                         Save genes in each of the clusters we annotated (ORA)
# -----------------------------------------------------------------------------------
for i in range(0, n_clusters):
    with open(f'{ora_dir}cluster_{i}_gene-names_{experiment_name}_{date}.csv', 'w+') as f:
        genes = df[gene_name].values[vae_set_idxs[i]]
        rank_vae0 = vae_data[:, 0][vae_set_idxs[i]]
        rank_vae1 = vae_data[:, 1][vae_set_idxs[i]]
        rank_vae2 = vae_data[:, 2][vae_set_idxs[i]]
        f.write(f'gene_name,VAE-1,VAE-2,VAE-3\n')
        for i, g in enumerate(genes):
            f.write(f'{g},{rank_vae0[i]},{rank_vae1[i]},{rank_vae2[i]}\n')
    

Plot Histone modification proflies for each group

Here we plot the histone modification profiles ofr each group

In [22]:
import string
tissues = ['forebrain', 'midbrain', 'hindbrain', 'neural-tube', 
           'embryonic-facial-prominence', 'limb',
           'heart', 'liver']
marks = ['H3K9me3', 'H3K36me3', 'H3K27me3', 'H3K27ac', 'H3K4me1', 'H3K4me2', 'H3K4me3','H3K9ac']
def get_median_naninc(df, mark, hist_metric="", time_1="", time_2="", time_3="", tissue="brain"):
    cols = []
    for c in df.columns:
        if tissue in c and mark in c and hist_metric in c and (time_1 in c or time_2 in c or time_3 in c):
            cols.append(c)
    # get nan median
    vals = np.nanmedian(df[cols].values, axis=1)
    return vals

def plot_mark_heatmap(df, idxs, title, mark_cutoff=3.0):
    mark_df = pd.DataFrame()
    mark_values = []
    mean_col = []
    if idxs is None:
        num_genes = len(df)
    else:
        num_genes = len(idxs) * 1.0
    for t in [['10.5', '11.5', '12.5'], ['15.5', '15.5', '16.5']]:
        tissue_titles = []
        marks_title = []
        for m in marks:
            mark_col = []
            for tissue in tissues:
                median_all_data = get_median_naninc(df, m, "signal", t[0], t[1], t[2], tissue)
                if idxs is None:
                    median_genes = median_all_data
                else:
                    median_genes = median_all_data[idxs]     
                has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
                mark_col.append(has_mark/num_genes) #np.nan_to_num(np.nanmean(median_genes)))
                if string.capwords(tissue) not in tissue_titles:
                    if tissue == 'embryonic-facial-prominence':
                        if 'E.F.P' not in tissue_titles:
                            tissue_titles.append('E.F.P')
                    else:
                        tissue_titles.append(string.capwords(tissue))
            marks_title.append(string.capwords(m))
            mark_df[string.capwords(m)] = mark_col

        mark_df['Tissue'] = tissue_titles

        heatmap = Heatmap(mark_df, marks_title, 'Tissue', vmin=0, vmax=None, cmap=hist_cmap, figsize=(2,2),
                          title=f'{title} {",".join(t)}', cluster_rows=False, cluster_cols=False)
        heatmap.plot()
        pplot()
        print(mark_df.head())
        # vmin=0, vmax=1,
        save_fig(f'mark_all_tissues_signal-{title}_{"-".join(t)}')
        plt.show()
        
for i, gs in enumerate(vae_set_idxs):
    plot_mark_heatmap(df, gs, f'Cluster {i}')
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
    H3k9me3  H3k36me3  H3k27me3   H3k27ac   H3k4me1   H3k4me2   H3k4me3  \
0  0.064516  0.032258  0.435484  0.032258  0.290323  0.225806  0.241935   
1  0.064516  0.032258  0.435484  0.016129  0.258065  0.193548  0.209677   
2  0.048387  0.032258  0.435484  0.032258  0.193548  0.193548  0.225806   
3  0.064516  0.032258  0.435484  0.161290  0.354839  0.403226  0.370968   
4  0.129032  0.032258  0.451613  0.080645  0.435484  0.290323  0.322581   

     H3k9ac       Tissue  
0  0.080645    Forebrain  
1  0.096774     Midbrain  
2  0.064516    Hindbrain  
3  0.258065  Neural-tube  
4  0.129032        E.F.P  
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
    H3k9me3  H3k36me3  H3k27me3   H3k27ac   H3k4me1   H3k4me2   H3k4me3  \
0  0.048387  0.000000  0.435484  0.048387  0.274194  0.225806  0.225806   
1  0.145161  0.032258  0.435484  0.048387  0.193548  0.241935  0.209677   
2  0.080645  0.016129  0.435484  0.064516  0.274194  0.306452  0.225806   
3  0.064516  0.016129  0.354839  0.258065  0.290323  0.435484  0.354839   
4  0.032258  0.032258  0.435484  0.080645  0.354839  0.354839  0.354839   

     H3k9ac       Tissue  
0  0.080645    Forebrain  
1  0.064516     Midbrain  
2  0.096774    Hindbrain  
3  0.193548  Neural-tube  
4  0.080645        E.F.P  
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
    H3k9me3  H3k36me3  H3k27me3   H3k27ac   H3k4me1   H3k4me2   H3k4me3  \
0  0.088889  0.533333  0.355556  0.861111  0.666667  0.950000  0.966667   
1  0.111111  0.555556  0.350000  0.844444  0.705556  0.944444  0.961111   
2  0.061111  0.427778  0.338889  0.883333  0.561111  0.944444  0.961111   
3  0.072222  0.488889  0.294444  0.722222  0.711111  0.966667  0.955556   
4  0.177778  0.661111  0.455556  0.972222  0.722222  0.972222  0.977778   

     H3k9ac       Tissue  
0  0.900000    Forebrain  
1  0.922222     Midbrain  
2  0.894444    Hindbrain  
3  0.922222  Neural-tube  
4  0.944444        E.F.P  
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
    H3k9me3  H3k36me3  H3k27me3   H3k27ac   H3k4me1   H3k4me2   H3k4me3  \
0  0.077778  0.183333  0.188889  0.672222  0.861111  0.905556  0.955556   
1  0.094444  0.150000  0.272222  0.683333  0.750000  0.961111  0.961111   
2  0.105556  0.188889  0.222222  0.683333  0.816667  0.972222  0.961111   
3  0.027778  0.077778  0.233333  0.733333  0.755556  0.966667  0.944444   
4  0.044444  0.327778  0.216667  0.738889  0.622222  0.966667  0.961111   

     H3k9ac       Tissue  
0  0.911111    Forebrain  
1  0.911111     Midbrain  
2  0.877778    Hindbrain  
3  0.811111  Neural-tube  
4  0.794444        E.F.P  
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
   H3k9me3  H3k36me3  H3k27me3  H3k27ac  H3k4me1  H3k4me2  H3k4me3  H3k9ac  \
0    0.025    0.0125    1.0000   0.0250   0.6000   0.5750   0.6250  0.1500   
1    0.000    0.0125    1.0000   0.0500   0.5625   0.5875   0.6500  0.1750   
2    0.025    0.0125    1.0000   0.3125   0.5125   0.7250   0.7875  0.3000   
3    0.025    0.1125    0.8125   0.5625   0.7500   0.9500   0.9250  0.8000   
4    0.050    0.0125    1.0000   0.2500   0.6500   0.6750   0.8250  0.4125   

        Tissue  
0    Forebrain  
1     Midbrain  
2    Hindbrain  
3  Neural-tube  
4        E.F.P  
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
   H3k9me3  H3k36me3  H3k27me3  H3k27ac  H3k4me1  H3k4me2  H3k4me3  H3k9ac  \
0   0.0500    0.0250    1.0000   0.0250   0.5875   0.5625   0.5750  0.1875   
1   0.0875    0.0250    1.0000   0.0375   0.4750   0.5750   0.5375  0.2000   
2   0.0625    0.0125    0.9750   0.3250   0.5375   0.7875   0.7750  0.4500   
3   0.0125    0.0000    0.6375   0.5250   0.7375   0.9125   0.8875  0.5500   
4   0.0125    0.0000    1.0000   0.1000   0.7000   0.7375   0.7750  0.1500   

        Tissue  
0    Forebrain  
1     Midbrain  
2    Hindbrain  
3  Neural-tube  
4        E.F.P  
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
    H3k9me3  H3k36me3  H3k27me3   H3k27ac   H3k4me1   H3k4me2   H3k4me3  \
0  0.058296  0.206278  0.000000  0.237668  0.215247  0.264574  0.251121   
1  0.062780  0.197309  0.000000  0.224215  0.255605  0.255605  0.246637   
2  0.044843  0.139013  0.000000  0.228700  0.188341  0.255605  0.255605   
3  0.035874  0.134529  0.000000  0.174888  0.206278  0.318386  0.246637   
4  0.134529  0.224215  0.017937  0.313901  0.327354  0.322870  0.300448   

     H3k9ac       Tissue  
0  0.228700    Forebrain  
1  0.233184     Midbrain  
2  0.219731    Hindbrain  
3  0.224215  Neural-tube  
4  0.269058        E.F.P  
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
    H3k9me3  H3k36me3  H3k27me3   H3k27ac   H3k4me1   H3k4me2   H3k4me3  \
0  0.053812  0.094170       0.0  0.224215  0.286996  0.291480  0.291480   
1  0.080717  0.044843       0.0  0.188341  0.251121  0.322870  0.269058   
2  0.076233  0.049327       0.0  0.228700  0.260090  0.336323  0.286996   
3  0.044843  0.044843       0.0  0.233184  0.255605  0.340807  0.269058   
4  0.017937  0.121076       0.0  0.295964  0.210762  0.349776  0.304933   

     H3k9ac       Tissue  
0  0.264574    Forebrain  
1  0.260090     Midbrain  
2  0.264574    Hindbrain  
3  0.219731  Neural-tube  
4  0.219731        E.F.P  
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
    H3k9me3  H3k36me3  H3k27me3   H3k27ac   H3k4me1   H3k4me2   H3k4me3  \
0  0.096154  0.064103  0.660256  0.198718  0.641026  0.660256  0.673077   
1  0.096154  0.096154  0.685897  0.217949  0.602564  0.673077  0.666667   
2  0.089744  0.064103  0.647436  0.391026  0.474359  0.705128  0.724359   
3  0.057692  0.141026  0.512821  0.384615  0.634615  0.846154  0.775641   
4  0.185897  0.153846  0.730769  0.416667  0.666667  0.711538  0.737179   

     H3k9ac       Tissue  
0  0.512821    Forebrain  
1  0.487179     Midbrain  
2  0.551282    Hindbrain  
3  0.756410  Neural-tube  
4  0.544872        E.F.P  
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
    H3k9me3  H3k36me3  H3k27me3   H3k27ac   H3k4me1   H3k4me2   H3k4me3  \
0  0.121795  0.032051  0.455128  0.294872  0.666667  0.679487  0.685897   
1  0.153846  0.185897  0.371795  0.467949  0.500000  0.717949  0.685897   
2  0.121795  0.153846  0.326923  0.634615  0.570513  0.826923  0.788462   
3  0.070513  0.076923  0.198718  0.717949  0.660256  0.852564  0.794872   
4  0.051282  0.089744  0.512821  0.237179  0.615385  0.743590  0.743590   

     H3k9ac       Tissue  
0  0.589744    Forebrain  
1  0.602564     Midbrain  
2  0.711538    Hindbrain  
3  0.737179  Neural-tube  
4  0.301282        E.F.P  
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
    H3k9me3  H3k36me3  H3k27me3   H3k27ac   H3k4me1   H3k4me2   H3k4me3  \
0  0.057851  0.157025  0.842975  0.396694  0.727273  0.892562  0.917355   
1  0.041322  0.173554  0.851240  0.280992  0.768595  0.826446  0.834711   
2  0.049587  0.090909  0.851240  0.231405  0.644628  0.801653  0.851240   
3  0.049587  0.099174  0.826446  0.115702  0.702479  0.876033  0.809917   
4  0.115702  0.223140  0.876033  0.628099  0.876033  0.892562  0.925620   

     H3k9ac       Tissue  
0  0.611570    Forebrain  
1  0.545455     Midbrain  
2  0.438017    Hindbrain  
3  0.619835  Neural-tube  
4  0.760331        E.F.P  
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
<ipython-input-22-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater
  has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
    H3k9me3  H3k36me3  H3k27me3   H3k27ac   H3k4me1   H3k4me2   H3k4me3  \
0  0.049587  0.066116  0.719008  0.190083  0.710744  0.760331  0.801653   
1  0.049587  0.057851  0.826446  0.157025  0.685950  0.851240  0.842975   
2  0.090909  0.049587  0.826446  0.049587  0.735537  0.826446  0.801653   
3  0.041322  0.041322  0.768595  0.090909  0.694215  0.818182  0.776860   
4  0.049587  0.049587  0.735537  0.355372  0.710744  0.867769  0.909091   

     H3k9ac       Tissue  
0  0.644628    Forebrain  
1  0.595041     Midbrain  
2  0.586777    Hindbrain  
3  0.371901  Neural-tube  
4  0.429752        E.F.P  

7) Inspecting the data in context of chromHMM annotations

Downloaded the pooled samples frmo http://enhancer.sdsc.edu/enhancer_export/ENCODE/chromHMM/pooled/ for forebrain, hindbrain, and midbrian at e16.5 on 04 December 2020.

http://enhancer.sdsc.edu/enhancer_export/ENCODE/chromHMM/readme

The files in this directory contain chromHMM chromatin state calls for multiple tissues during mouse embryonic development, as described here: http://www.biorxiv.org/content/early/2017/07/21/166652.

All files are in bed format. There is one file for each tissue at each developmental stage. The columns are 1) chromosome, 2) regions start, 3) region end, 4) chromatin state number 5) Chromatin state label. There are 15 chromating states, labeled as follows:

    State   Label   Description
    1   Pr-A    Promoter, Active
    2   Pr-W    Promoter, Weak/Inactive
    3   Pr-B    Promoter, Bivalent
    4   Pr-F    Promoter, Flanking
    5   En-Sd   Enhancer, Strong TSS-distal
    6   En-Sp   Enhancer, Strong TSS-proximal
    7   En-W    Enhancer, Weak TSS-distal
    8   En-Pd   Enhancer, Poised TSS-distal
    9   En-Pp   Enhancer, Poised TSS-proximal
    10  Tr-S    Transcription, Strong
    11  Tr-P    Transcription, Permissive
    12  Tr-I    Transcription, Initiation
    13  Hc-P    Heterochromatin, Polycomb-associated
    14  Hc-H    Heterochromatin, H3K9me3-associated
    15  NS  No Chromatin Signal
    NRS NRS No Reproducible State (ony relevant to "replicated" call set)


There are four subdirectories:

rep1\ This subdirectory contains chromatin state calls made using ChIP-seq datasets from biological replicate 1 (two biological replicates were performed for each ChIP-seq experiment).

rep2\ This subdirectory contains chromatin state calls made using ChIP-seq datasets from biological replicate 2.

replicated\ This subdirectory contains the intersection of the replicate 1 and replicate 2 chromatin state calls. Here, we require that a region is called in the same state in both replicates. If a region is not called in the same state in both replicates, it is labeled "NRS" for No Reproducible Signal. Note this is differenct than state 15, which is "No Signal."

pooled\ This subdirectory contains chromatin state calls on ChIP-seq data pooled from both replicates. We expect this will be the desired set of files for most users.

Each subdirectory has two additional subdirectorites:

archive\ This contains an older version of the bed files in which there are only state number, no labels. Aside from the labels, the files are identical to the working versions in the main directory.

tracks\ This contains bigBed versions of the files for visualization on the UCSC Genome Browser.

There is one additional subdirectory that does not conform to the templates decribed above. This is 6_mark_models. This subdirectory contains chromHMM segmentation of the e10.5 stage where we did not have all 8 marks. Note that the 11-state and 16-states models used for this segmentation are distinct from the 15-state (8-mark) model described above for the other stages. Please see manuscript and Extended Data Figure 7 for additional information.

    header = "chr,start,end,label,annotation"
    bed = Bed(os.path.join(self.data_dir, 'e16.5_forebrain_15_segments.bed'),
              header=None, overlap_method='in_promoter',
              output_bed_file=os.path.join(self.data_dir, 'e16.5_forebrain_15_segments_selectedPeaks.bed'),
              buffer_after_tss=0, buffer_before_tss=10, buffer_gene_overlap=0,
              gene_start=3, gene_end=4, gene_chr=2, gene_direction=5, gene_name=0,
              chr_idx=0, start_idx=1, end_idx=2, peak_value=4, header_extra="3", sep='\t'
              )
    # Add the gene annot
    bed.set_annotation_from_file(self.mm10_annot)
    # Now we can run the assign values
    bed.assign_locations_to_genes()
    bed.save_loc_to_csv(f'{self.data_dir}test_e16.5_forebrain_15_segments.csv')
In [23]:
# -----------------------------------------------------------------------------------
#                         Merge information with annotations
# -----------------------------------------------------------------------------------

chrom_dir = os.path.join(supp_dir, "annot")
chromhmm_annot = pd.read_csv(f'{chrom_dir}/e16.5_forebrain_15_segments.csv')
print(len(chromhmm_annot), len(df))
# Make sure we only keep 1 annotation per gene
chromhmm_annot = chromhmm_annot.groupby('external_gene_name').first()
# Merge this with our dataframe
df = df.merge(chromhmm_annot, on='external_gene_name', how='left', suffixes=('', '_chmm'))
# Ensure the new length is the same as the old length
len(df)
53254 1371
Out[23]:
1371
In [24]:
# We also want to plot the annotations from gorkin et al for each of the clusters
from statsmodels.stats.multitest import multipletests

def run_annot_plot(df_bg, df_fg, title=""):
    changes = []
    order = ['Pr-A', 'Pr-W', 'Pr-B', 'Pr-F', 'En-Sd', 'En-Sp', 
         'En-W', 'En-Pd', 'En-Pp', 'Tr-S', 'Tr-P', 'Tr-I', 
          'Hc-P', 'Hc-H', 'NS']
    total = len(df_bg) * 1.0
    total_sig = len(df_fg) * 1.0
    perc_all = []
    perc_sig = []
    
    for o in order:
        o_all = len(df_bg[df_bg['peak_value'] == o])
        o_sig = len(df_fg[df_fg['peak_value'] == o])
        perc_all.append(o_all)
        perc_sig.append(o_sig)
        if o_all == 0 and o_sig == 0:
            changes.append(0)
        else:
            changes.append(((o_sig) - (o_all)))

    odds_ratios = []
    pvalues = []
    for i, o in enumerate(order):
        # Do a FET on each one
        oddsratio, pvalue = stats.fisher_exact([[perc_sig[i], perc_all[i]], 
                                                [total_sig - perc_sig[i],
                                                 total - perc_all[i]]])

        print(o, oddsratio, pvalue,[[perc_sig[i], perc_all[i]], 
                                                [total_sig - perc_sig[i], 
                                                 total - perc_all[i]]])
        odds_ratios.append(oddsratio)
        pvalues.append(pvalue)

    reg, padj, a, b = multipletests(pvalues, alpha=0.1, 
                                    method='fdr_bh', returnsorted=False)

    p_sigs = []
    for p in padj: 
        if p > 0.05:
            p_sigs.append('')
        elif p <= 0.05 and p > 0.01:
            p_sigs.append('*')
        elif p <= 0.01 and p > 0.001:
            p_sigs.append('**')
        elif p <= 0.001 and p > 0.0001:
            p_sigs.append('***')
        elif p <= 0.0001:
            p_sigs.append('****')
        else:
            print(p)
            p_sigs.append('')

    fig, ax = plt.subplots(figsize=(1.5,1.0))
    c = [grey] * 14
    c[2] = "green"
    c[12] = "green"
    plt.bar(order, odds_ratios, color=c, linewidth=0.5, edgecolor='black')
    rects = ax.patches
    # Make some labels.
    labels = [f'{p_sigs[i]}' for i in range(0, len(rects))]

    for rect, label in zip(rects, labels):
        height = rect.get_height()
        ax.text(rect.get_x() + rect.get_width() / 2, height, label,
                ha='center', va='bottom')
    plt.ylim(0, 7)
    plt.yticks(np.arange(0, 7, 2.0))
    ax.set_title(f'Odds ratio FET for significant dataset {title} compared to all genes ChromHMM annotations')
    ax.set_xlabel('', fontsize=6)
    ax.set_ylabel('Odds ratio', fontsize=6)
    ax.set_xticklabels(order, rotation=90, ha="center")

        
    ax.tick_params(direction='out', length=2, width=0.5)
    ax.spines['bottom'].set_linewidth(0.5)
    ax.spines['top'].set_linewidth(0)
    ax.spines['left'].set_linewidth(0.5)
    ax.spines['right'].set_linewidth(0)
    ax.tick_params(labelsize=6)
    ax.tick_params(axis='x', which='major', pad=0)
    ax.tick_params(axis='y', which='major', pad=0)

    save_fig(f'OR-CM-{title}')
    
    plt.show()

for gene_set_label in vae_set_labels:
    run_annot_plot(df, df[df[gene_set_label] == True], title=f'Training vs {gene_set_label}')
Pr-A 0.24802314368370298 0.17457840756079304 [[1, 85], [61.0, 1286.0]]
Pr-W 0.0 0.0019815664944420985 [[0, 147], [62.0, 1224.0]]
Pr-B 0.5069657744624836 0.06957588473414678 [[9, 344], [53.0, 1027.0]]
Pr-F 0.0 0.0007674533464589754 [[0, 164], [62.0, 1207.0]]
En-Sd 0.0 1.0 [[0, 7], [62.0, 1364.0]]
En-Sp 0.0 0.6246954710395699 [[0, 26], [62.0, 1345.0]]
En-W 0.0 1.0 [[0, 4], [62.0, 1367.0]]
En-Pd 1.598809523809524 0.3757481029111142 [[2, 28], [60.0, 1343.0]]
En-Pp 0.0 0.4009217452840129 [[0, 36], [62.0, 1335.0]]
Tr-S 0.0 1.0 [[0, 4], [62.0, 1367.0]]
Tr-P 1.5675865953252606 0.27383679350529067 [[9, 134], [53.0, 1237.0]]
Tr-I 0.0 1.0 [[0, 15], [62.0, 1356.0]]
Hc-P 2.7622115672759966 0.0025213384922534265 [[15, 142], [47.0, 1229.0]]
Hc-H 11.221311475409836 0.124345814356102 [[1, 2], [61.0, 1369.0]]
NS 3.254421768707483 9.016987208538535e-05 [[20, 175], [42.0, 1196.0]]
<ipython-input-24-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_xticklabels(order, rotation=90, ha="center")
Pr-A 5.501604278074867 4.369599661953072e-15 [[48, 85], [132.0, 1286.0]]
Pr-W 3.38265306122449 6.965518380881973e-10 [[52, 147], [128.0, 1224.0]]
Pr-B 0.29126488939308 1.789748823125851e-07 [[16, 344], [164.0, 1027.0]]
Pr-F 1.2425562242635413 0.3342723653858386 [[26, 164], [154.0, 1207.0]]
En-Sd 0.0 1.0 [[0, 7], [180.0, 1364.0]]
En-Sp 4.026946107784431 0.00024000238512966222 [[13, 26], [167.0, 1345.0]]
En-W 1.9092178770949721 0.4607875660485072 [[1, 4], [179.0, 1367.0]]
En-Pd 0.26795690343176376 0.24214321559289073 [[1, 28], [179.0, 1343.0]]
En-Pp 0.8428030303030303 1.0 [[4, 36], [176.0, 1335.0]]
Tr-S 0.0 1.0 [[0, 4], [180.0, 1367.0]]
Tr-P 0.05157175018760944 8.291476909574294e-07 [[1, 134], [179.0, 1237.0]]
Tr-I 1.5322033898305085 0.45569407463635647 [[3, 15], [177.0, 1356.0]]
Hc-P 0.14669372165194558 1.774474486131201e-05 [[3, 142], [177.0, 1229.0]]
Hc-H 0.0 1.0 [[0, 2], [180.0, 1369.0]]
NS 0.11583535108958838 3.58631919432044e-07 [[3, 175], [177.0, 1196.0]]
<ipython-input-24-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_xticklabels(order, rotation=90, ha="center")
Pr-A 0.19151154132539092 0.08376742654843522 [[1, 85], [79.0, 1286.0]]
Pr-W 0.0 0.0003577056615543604 [[0, 147], [80.0, 1224.0]]
Pr-B 2.0961776348342407 0.0023318229337622215 [[33, 344], [47.0, 1027.0]]
Pr-F 0.0 8.143390424405342e-05 [[0, 164], [80.0, 1207.0]]
En-Sd 0.0 1.0 [[0, 7], [80.0, 1364.0]]
En-Sp 0.0 0.3950490840406473 [[0, 26], [80.0, 1345.0]]
En-W 0.0 1.0 [[0, 4], [80.0, 1367.0]]
En-Pd 0.0 0.39870718835415564 [[0, 28], [80.0, 1343.0]]
En-Pp 0.0 0.25896549663402646 [[0, 36], [80.0, 1335.0]]
Tr-S 0.0 1.0 [[0, 4], [80.0, 1367.0]]
Tr-P 0.0 0.0005323880338418577 [[0, 134], [80.0, 1237.0]]
Tr-I 0.0 1.0 [[0, 15], [80.0, 1356.0]]
Hc-P 9.565974796145293 4.412021222573505e-19 [[42, 142], [38.0, 1229.0]]
Hc-H 0.0 1.0 [[0, 2], [80.0, 1369.0]]
NS 0.0 5.188498522240626e-05 [[0, 175], [80.0, 1196.0]]
<ipython-input-24-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_xticklabels(order, rotation=90, ha="center")
Pr-A 1.2485436893203883 0.4595642158711125 [[17, 85], [206.0, 1286.0]]
Pr-W 1.1956043956043956 0.4190882738128393 [[28, 147], [195.0, 1224.0]]
Pr-B 0.0 4.2162756122430757e-26 [[0, 344], [223.0, 1027.0]]
Pr-F 0.13442476890522329 2.0652404898182156e-07 [[4, 164], [219.0, 1207.0]]
En-Sd 0.8777348777348777 1.0 [[1, 7], [222.0, 1364.0]]
En-Sp 1.1864855328158082 0.7921397673155959 [[5, 26], [218.0, 1345.0]]
En-W 0.0 1.0 [[0, 4], [223.0, 1367.0]]
En-Pd 1.326201448321264 0.46145943879012286 [[6, 28], [217.0, 1343.0]]
En-Pp 0.6773211567732116 0.6440197216845152 [[4, 36], [219.0, 1335.0]]
Tr-S 1.5394144144144144 0.529779431950093 [[1, 4], [222.0, 1367.0]]
Tr-P 1.7771569957698141 0.006785427448538306 [[36, 134], [187.0, 1237.0]]
Tr-I 0.0 0.24959165623171417 [[0, 15], [223.0, 1356.0]]
Hc-P 0.0 3.7027908588664884e-10 [[0, 142], [223.0, 1229.0]]
Hc-H 6.1945701357466065 0.09644920300090952 [[2, 2], [221.0, 1369.0]]
NS 6.191746031746032 7.710881518533144e-30 [[106, 175], [117.0, 1196.0]]
<ipython-input-24-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_xticklabels(order, rotation=90, ha="center")
Pr-A 1.1477484787018255 0.6052307709708241 [[11, 85], [145.0, 1286.0]]
Pr-W 1.1547743184865187 0.5868947688092181 [[19, 147], [137.0, 1224.0]]
Pr-B 0.8009784458309699 0.32705749945288565 [[33, 344], [123.0, 1027.0]]
Pr-F 1.540414066931367 0.07246092554839278 [[27, 164], [129.0, 1207.0]]
En-Sd 2.5306122448979593 0.2324097507581155 [[2, 7], [154.0, 1364.0]]
En-Sp 1.0143288084464555 1.0 [[3, 26], [153.0, 1345.0]]
En-W 2.2048387096774196 0.4170010167725652 [[1, 4], [155.0, 1367.0]]
En-Pd 1.5882213812677388 0.37520130787198747 [[5, 28], [151.0, 1343.0]]
En-Pp 0.7271241830065359 0.7911379988646342 [[3, 36], [153.0, 1335.0]]
Tr-S 2.2048387096774196 0.4170010167725652 [[1, 4], [155.0, 1367.0]]
Tr-P 0.7692786069651741 0.4736439725646061 [[12, 134], [144.0, 1237.0]]
Tr-I 1.174025974025974 0.6898496143650471 [[2, 15], [154.0, 1356.0]]
Hc-P 1.6517041178367917 0.041197404546333624 [[25, 142], [131.0, 1229.0]]
Hc-H 0.0 1.0 [[0, 2], [156.0, 1369.0]]
NS 0.3210738255033557 0.001545060904323077 [[7, 175], [149.0, 1196.0]]
<ipython-input-24-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_xticklabels(order, rotation=90, ha="center")
Pr-A 0.7893606138107417 0.6952526891058393 [[6, 85], [115.0, 1286.0]]
Pr-W 0.5112781954887218 0.11677826270018386 [[7, 147], [114.0, 1224.0]]
Pr-B 1.7677096083231334 0.0048695282776436925 [[45, 344], [76.0, 1027.0]]
Pr-F 0.810248377713135 0.6584862370606197 [[12, 164], [109.0, 1207.0]]
En-Sd 1.6238095238095238 0.4925103289011059 [[1, 7], [120.0, 1364.0]]
En-Sp 3.176450742240216 0.013965822833765887 [[7, 26], [114.0, 1345.0]]
En-W 0.0 1.0 [[0, 4], [121.0, 1367.0]]
En-Pd 0.8061224489795918 1.0 [[2, 28], [119.0, 1343.0]]
En-Pp 0.0 0.1110542623477057 [[0, 36], [121.0, 1335.0]]
Tr-S 0.0 1.0 [[0, 4], [121.0, 1367.0]]
Tr-P 0.15514862661482504 0.0013656410857616024 [[2, 134], [119.0, 1237.0]]
Tr-I 0.0 0.6262248512800649 [[0, 15], [121.0, 1356.0]]
Hc-P 2.4859904105483968 0.00026011625702471885 [[27, 142], [94.0, 1229.0]]
Hc-H 0.0 1.0 [[0, 2], [121.0, 1369.0]]
NS 0.056952380952380956 5.0284641066378245e-06 [[1, 175], [120.0, 1196.0]]
<ipython-input-24-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_xticklabels(order, rotation=90, ha="center")
In [25]:
df_bg = df_all.copy()  # Make a copy so we don't override the other one

# Also have a look at how the marks plot over genes that are not significant
chrom_dir = os.path.join(supp_dir, "annot")
chromhmm_annot = pd.read_csv(f'{chrom_dir}/e16.5_forebrain_15_segments.csv')
print(len(chromhmm_annot), len(df_bg))
# Make sure we only keep 1 annotation per gene
chromhmm_annot = chromhmm_annot.groupby('external_gene_name').first()
# Merge this with our dataframe
df_bg = df_bg.merge(chromhmm_annot, on='external_gene_name', how='left', suffixes=('', '_chmm'))
# Ensure the new length is the same as the old length
run_annot_plot(df_bg, df, title=f'Training vs All')
53254 20900
Pr-A 0.16607420573205955 2.148953216275548e-92 [[85, 5950], [1286.0, 14950.0]]
Pr-W 0.5954233460773135 9.414743557547487e-10 [[147, 3508], [1224.0, 17392.0]]
Pr-B 7.495674942111099 2.2492063227528104e-139 [[344, 894], [1027.0, 20006.0]]
Pr-F 2.4410479101791274 2.9726993079622145e-20 [[164, 1102], [1207.0, 19798.0]]
En-Sd 1.572192513368984 0.22811405425132192 [[7, 68], [1364.0, 20832.0]]
En-Sp 1.1060587546986156 0.5926797643301734 [[26, 359], [1345.0, 20541.0]]
En-W 1.7443829031246734 0.3009362133436212 [[4, 35], [1367.0, 20865.0]]
En-Pd 2.157855547282204 0.00044861531889056703 [[28, 200], [1343.0, 20700.0]]
En-Pp 1.4407303370786517 0.050404632283021236 [[36, 384], [1335.0, 20516.0]]
Tr-S 0.870728393771554 1.0 [[4, 70], [1367.0, 20830.0]]
Tr-P 1.1432078804754633 0.15221518913175552 [[134, 1809], [1237.0, 19091.0]]
Tr-I 1.8384955752212389 0.032705856267117785 [[15, 125], [1356.0, 20775.0]]
Hc-P 5.861708384019851 2.776409677538453e-51 [[142, 404], [1229.0, 20496.0]]
Hc-H 0.12165696646952096 2.972537104552533e-05 [[2, 248], [1369.0, 20652.0]]
NS 0.47486730431249946 1.7301030502620268e-22 [[175, 4923], [1196.0, 15977.0]]
<ipython-input-24-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_xticklabels(order, rotation=90, ha="center")

) Epi genes annotation

Using the information from: https://epifactors.autosome.ru/ (EpiFactors: a comprehensive database of human epigenetic factors and complexes, by Yulia A. Medvedeva et al) we test each cluser for enrichment for epigenetic complex's and functions.

In [26]:
epi_genes = pd.read_csv(os.path.join(supp_dir, 'EpiGenes_main.csv'))
df_epi = df.merge(epi_genes, left_on='external_gene_name', right_on='MGI_symbol',
                      how='left', suffixes=('', '_eg'))
    
ax = sns.countplot(x="Function", data=df_epi)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
save_fig(f'Epigenes-function')
plt.show() 
ax = sns.countplot(x="Target", data=df_epi)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
save_fig(f'Epigenes-target')
plt.show() 

for set_label in vae_set_labels:
    try:
        vae_mse.u.dp([set_label])
        ax = sns.countplot(x="Function", data=df_epi[df_epi[set_label] == True])
        ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
        plt.tight_layout()
        save_fig(f'Epigenes-function-{set_label}')
        plt.show() 
        ax = sns.countplot(x="Target", data=df_epi[df_epi[set_label] == True])
        ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
        plt.tight_layout()
        save_fig(f'Epigenes-target-{set_label}')
        plt.show() 
        # Complex_name Modification
        ax = sns.countplot(x="Complex_name", data=df_epi[df_epi[set_label] == True])
        ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
        plt.tight_layout()
        save_fig(f'Epigenes-Complex_name-{set_label}')
        plt.show() 
        ax = sns.countplot(x="Modification", data=df_epi[df_epi[set_label] == True])
        ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
        plt.tight_layout()
        save_fig(f'Epigenes-Modification-{set_label}')
        plt.show() 
    except:
        print(set_label)
<ipython-input-26-4ffd89933e6f>:7: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. 
  plt.tight_layout()
--------------------------------------------------------------------------------
                                     Set 1	                                     
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
                                     Set 2	                                     
--------------------------------------------------------------------------------
<ipython-input-26-4ffd89933e6f>:21: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. 
  plt.tight_layout()
--------------------------------------------------------------------------------
                                     Set 3	                                     
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
                                     Set 4	                                     
--------------------------------------------------------------------------------
<ipython-input-26-4ffd89933e6f>:21: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. 
  plt.tight_layout()
--------------------------------------------------------------------------------
                                     Set 5	                                     
--------------------------------------------------------------------------------
<ipython-input-26-4ffd89933e6f>:21: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. 
  plt.tight_layout()
<ipython-input-26-4ffd89933e6f>:37: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. 
  plt.tight_layout()
--------------------------------------------------------------------------------
                                     Set 6	                                     
--------------------------------------------------------------------------------
Set 6

Analysis of the cell cycle genes

Anti-proliferative genes:

genes = [
'Cdkn1a', 'Cdkn1b', 'Cdkn2a', 'Cdkn1c', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Rb1', 'E2f1', 'E2f2', 'E2f3', 'Cdk2', 'Cdk4', 'Cdk6'
]

Pro-prolifferative genes:

genes = ['Ccna1', 'Ccna2', 'Ccnb1', 'Ccnb2', 'Ccnb3', 'Cdc25a', 'Cdc25b', 'Cdc25c', 'Cdk1', 'Chek1', 'Chek2', 'Wee1', 'Wee2']
In [27]:
div_cmap = 'seismic' #'RdBu_r'
H3k_cols = [
 'forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal',
 'forebrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF592HST_signal',
 'forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal',
 'forebrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF076ZNJ_signal',
 'forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal',
 'forebrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF082ONF_signal',
 'forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal',
            
 'midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal',
 'midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal',
 'midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal',
 'midbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF276UEL_signal',
 'midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal',
 'midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal',
 'midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal',
            
 'hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal',
 'hindbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF809JLW_signal',
 'hindbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF435HPM_signal',
 'hindbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF324KBG_signal',
 'hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal',
 'hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal',
 'hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal']

wt_cols = [c for c in df_all.columns if 'wt' in c and 'merged' in c]
ko_cols = [c for c in df_all.columns if 'ko' in c and 'merged' in c]
logfc = [c for c in df_all.columns if 'log2Fold' in c]

mean_h3k = np.nanmean(-1 * df_all[logfc].values, axis=1)
desc_sorted = (mean_h3k).argsort()  # Sort the genes by descending order
gene_names_sorted = df_all[gene_name].values[desc_sorted]

genes = ['Cdkn1a', 'Cdkn1b', 'Cdkn2a', 'Cdkn1c', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Rb1', 'E2f1', 'E2f2', 'E2f3', 'Cdk2', 'Cdk4', 'Cdk6']
genes2 = [] 
genes4 = [] 
genes3 = []

tst_lbl = 'anti-pro'
values = df_all[H3k_cols].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  H3k_cols, vae_data, vae_mse,
                  gene_name, mark='H3K27me3', method='input', 
                  title=f'WT HIST {tst_lbl} {experiment_name}', cmap=hist_cmap, 
                  genes=gene_names_sorted, values=values)

values = df_all[wt_cols].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  wt_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
                  method='input', title=f'WT {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

values = df_all[logfc].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  logfc, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=div_cmap,
                  method='input', title=f'LogFC {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

values = df_all[ko_cols].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  ko_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
                  method='input', title=f'KO {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

h3k_cols = [c for c in df_all if 'H3K27me3' in c and 'median' not in c and 'signal' in c and 'brain' in c]
h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
u = SciUtil()
u.dp([tst_lbl, 'Number of marked genes:'])
print(len(set(genes) & set(h3k_const_aff)), len(set(genes)))
  external_gene_name  \
0             Cdkn2a   
1             Cdkn2b   
2             Cdkn2c   
3              Ccnd1   
4             Cdkn1a   

   forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal  \
0                                           11.93962                    
1                                           17.47897                    
2                                            4.07843                    
3                                            2.60924                    
4                                            3.69001                    

   forebrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF592HST_signal  \
0                                            8.82170                    
1                                            3.53901                    
2                                            0.00000                    
3                                            0.00000                    
4                                            0.00000                    

   forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal  \
0                                            8.03731                    
1                                            5.40300                    
2                                            0.00000                    
3                                            3.31166                    
4                                            2.75775                    

   forebrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF076ZNJ_signal  \
0                                            6.52177                    
1                                            6.20672                    
2                                            0.00000                    
3                                            3.38683                    
4                                            2.35830                    

   forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal  \
0                                            6.65861                    
1                                            3.56529                    
2                                            0.00000                    
3                                            3.98635                    
4                                            0.00000                    

   forebrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF082ONF_signal  \
0                                            6.44049                    
1                                            3.70542                    
2                                            0.00000                    
3                                            2.99581                    
4                                            0.00000                    

   forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal  \
0                                            5.90222                    
1                                            4.07982                    
2                                            0.00000                    
3                                            4.36289                    
4                                            0.00000                    

   midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal  \
0                                           12.34218                   
1                                           12.34218                   
2                                            3.68897                   
3                                            2.98231                   
4                                            2.74551                   

   midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal  ...  \
0                                            9.26312                  ...   
1                                            5.85112                  ...   
2                                            0.00000                  ...   
3                                            4.79700                  ...   
4                                            0.00000                  ...   

   midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal  \
0                                            7.00107                   
1                                            5.97121                   
2                                            0.00000                   
3                                            4.51583                   
4                                            4.04349                   

   midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal  \
0                                            3.25848                   
1                                            3.74599                   
2                                            1.98927                   
3                                            3.33553                   
4                                            0.00000                   

   midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal  \
0                                            3.51227                   
1                                            3.62944                   
2                                            0.00000                   
3                                            3.13656                   
4                                            0.00000                   

   hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal  \
0                                           16.79157                    
1                                           16.79157                    
2                                            4.41238                    
3                                            3.30582                    
4                                            3.18672                    

   hindbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF809JLW_signal  \
0                                            5.11687                    
1                                            3.88986                    
2                                            3.92628                    
3                                            3.59867                    
4                                            0.00000                    

   hindbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF435HPM_signal  \
0                                            8.48420                    
1                                            6.30277                    
2                                            2.79285                    
3                                            4.23949                    
4                                            2.54445                    

   hindbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF324KBG_signal  \
0                                            9.45087                    
1                                            4.94381                    
2                                            3.15347                    
3                                            5.49069                    
4                                            3.20577                    

   hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal  \
0                                           11.49704                    
1                                           12.48229                    
2                                            5.18150                    
3                                            6.70107                    
4                                            0.00000                    

   hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal  \
0                                            3.93875                    
1                                            5.71146                    
2                                            0.00000                    
3                                            2.69923                    
4                                            3.04388                    

   hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal  
0                                            4.55180                   
1                                            4.97978                   
2                                            0.00000                   
3                                            4.41730                   
4                                            0.00000                   

[5 rows x 22 columns]
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/matrix.py:1205: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. 
  self.fig.tight_layout(**tight_params)
  external_gene_name  wt11fb_merged-rep  wt13fb_merged-rep  wt15fb_merged-rep  \
0             Cdkn2a           0.081965           0.055547           0.163438   
1             Cdkn2b           0.081965           0.350432           0.413053   
2             Cdkn2c           2.795972           3.929023           3.936722   
3              Ccnd1           9.411207           8.575561           6.771200   
4             Cdkn1a           3.908049           4.656218           4.713217   

   wt18fb_merged-rep  wt11mb_merged-rep  wt13mb_merged-rep  wt15mb_merged-rep  \
0           0.171822           0.033312           0.070967           0.049808   
1           0.723561           0.071978           0.208532           0.523580   
2           3.197610           3.002832           2.874355           2.106403   
3           6.230616           9.440191           8.787826           6.684029   
4           5.040493           3.747754           4.591435           5.431125   

   wt18mb_merged-rep  wt11hb_merged-rep  wt13hb_merged-rep  wt15hb_merged-rep  \
0           0.093461           0.085673           0.054281           0.014650   
1           0.713752           0.904698           0.369921           0.374290   
2           2.271730           2.516919           1.587441           1.845625   
3           6.157662           9.123114           6.804078           5.681080   
4           6.344001           3.925292           4.424438           5.682118   

   wt18hb_merged-rep  wt11sc_merged-rep  wt13sc_merged-rep  wt15sc_merged-rep  \
0           0.081343           0.053746           0.037974           0.096582   
1           0.940972           0.329959           0.441814           0.658737   
2           2.568907           1.944132           1.793325           2.217030   
3           5.835546           8.934253           6.932590           6.524543   
4           6.241381           4.776748           4.154204           5.327085   

   wt18sc_merged-rep  
0           0.126187  
1           1.080613  
2           2.713289  
3           5.529480  
4           5.995773  
  external_gene_name  log2FoldChange_fb  log2FoldChange_mb  log2FoldChange_hb  \
0             Cdkn2a           8.198440           8.680234           9.364038   
1             Cdkn2b           4.714339           4.866714           4.822436   
2             Cdkn2c           0.110017           0.874814           1.128081   
3              Ccnd1           1.024355           0.475613           1.523859   
4             Cdkn1a           1.566764           0.549537           0.735342   

   log2FoldChange_sc  log2FoldChange_a11  log2FoldChange_a13  \
0           7.681831            7.625755            9.294939   
1           3.512037            4.349099            5.254427   
2           0.848435            0.969480            0.517564   
3           0.673827            0.196165           -0.356187   
4           0.733478            0.465161            1.465222   

   log2FoldChange_a15  log2FoldChange_a18  log2FoldChange_p11  \
0            7.952704            8.154555            7.221034   
1            4.802115            4.582345            1.099182   
2            0.465040            0.587984            1.375452   
3            1.061552            1.617141            0.339446   
4            0.798905            1.023042            0.279383   

   log2FoldChange_p13  log2FoldChange_p15  log2FoldChange_p18  
0            8.982836            8.505726            8.211635  
1            4.373813            4.582029            3.710329  
2            1.435444            1.090585            0.566230  
3            0.539246            1.092288            1.771155  
4            1.163413            0.452229            0.696539  
  external_gene_name  ko11fb_merged-rep  ko13fb_merged-rep  ko15fb_merged-rep  \
0             Cdkn2a           3.228548           5.254777           4.544105   
1             Cdkn2b           1.115676           3.146196           3.518678   
2             Cdkn2c           4.031161           4.373202           3.684748   
3              Ccnd1           9.725485           8.594899           8.116702   
4             Cdkn1a           4.395186           6.475518           6.083869   

   ko18fb_merged-rep  ko11mb_merged-rep  ko13mb_merged-rep  ko15mb_merged-rep  \
0           4.884756           2.923626           4.524850           4.166973   
1           3.996336           0.996446           3.166510           3.585624   
2           3.407147           3.511233           3.273202           3.146505   
3           7.966551           9.472000           7.954697           7.447099   
4           6.592410           4.080616           5.514889           5.684844   

   ko18mb_merged-rep  ko11hb_merged-rep  ko13hb_merged-rep  ko15hb_merged-rep  \
0           4.697869           3.470524           4.287647           4.185092   
1           4.128003           1.328320           3.224278           3.783847   
2           3.082391           3.820127           2.749030           2.870688   
3           7.588084           9.531941           7.618633           7.488938   
4           6.805126           4.627656           5.528786           6.074460   

   ko18hb_merged-rep  ko11sc_merged-rep  ko13sc_merged-rep  ko15sc_merged-rep  \
0           4.857104           2.219755           3.732213           3.541950   
1           4.263627           0.803604           2.518129           3.144523   
2           3.084977           2.910008           2.731408           2.959750   
3           7.712941           9.226480           7.025576           6.940598   
4           6.933814           4.588550           5.127861           5.801270   

   ko18sc_merged-rep  
0           4.204403  
1           3.449436  
2           3.204036  
3           7.159267  
4           6.727258  
--------------------------------------------------------------------------------
                       anti-pro	Number of marked genes:	                        
--------------------------------------------------------------------------------
18 19
<ipython-input-27-ef3cf465390b>:70: RuntimeWarning: Mean of empty slice
  h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
<ipython-input-27-ef3cf465390b>:70: RuntimeWarning: invalid value encountered in greater
  h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
In [28]:
# Sort the genes by average H3K27me3
mean_h3k = np.nanmean(df_all[logfc].values, axis=1)
desc_sorted = (mean_h3k).argsort()  # Sort the genes by descending order
gene_names_sorted = df_all[gene_name].values[desc_sorted]

genes = ['Ccna1', 'Ccna2', 'Ccnb1', 'Ccnb2', 'Ccnb3', 'Cdc25a', 'Cdc25b', 'Cdc25c', 'Cdk1', 'Chek1', 'Chek2', 'Wee1', 'Wee2']
genes2 = [] 
genes4 = [] 
genes3 = []

tst_lbl = 'pro-pro'
values = df_all[H3k_cols].values[desc_sorted]

plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  H3k_cols, vae_data, vae_mse,
                  gene_name, mark='H3K27me3', method='input', 
                  title=f'WT HIST {tst_lbl} {experiment_name}', cmap=hist_cmap, 
                  genes=gene_names_sorted, values=values)

values = df_all[wt_cols].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  wt_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
                  method='input', title=f'WT {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

values = df_all[logfc].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  logfc, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=div_cmap,
                  method='input', title=f'LogFC {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

values = df_all[ko_cols].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  ko_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
                  method='input', title=f'KO {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

h3k_cols = [c for c in df_all if 'H3K27me3' in c and 'median' not in c and 'signal' in c and 'brain' in c]
h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
u = SciUtil()
u.dp([tst_lbl, 'Number of marked genes:'])
print(len(set(genes) & set(h3k_const_aff)), len(set(genes)))
  external_gene_name  \
0             Cdc25c   
1              Ccnb1   
2              Ccna2   
3              Chek1   
4               Cdk1   

   forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   forebrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF592HST_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   forebrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF076ZNJ_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   forebrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF082ONF_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal  \
0                                                0.0                   
1                                                0.0                   
2                                                0.0                   
3                                                0.0                   
4                                                0.0                   

   midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal  ...  \
0                                                0.0                  ...   
1                                                0.0                  ...   
2                                                0.0                  ...   
3                                                0.0                  ...   
4                                                0.0                  ...   

   midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal  \
0                                                0.0                   
1                                                0.0                   
2                                                0.0                   
3                                                0.0                   
4                                                0.0                   

   midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal  \
0                                                0.0                   
1                                                0.0                   
2                                                0.0                   
3                                                0.0                   
4                                                0.0                   

   midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal  \
0                                                0.0                   
1                                                0.0                   
2                                                0.0                   
3                                                0.0                   
4                                                0.0                   

   hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   hindbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF809JLW_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   hindbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF435HPM_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   hindbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF324KBG_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal  
0                                                0.0                   
1                                                0.0                   
2                                                0.0                   
3                                                0.0                   
4                                                0.0                   

[5 rows x 22 columns]
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/matrix.py:1205: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. 
  self.fig.tight_layout(**tight_params)
  external_gene_name  wt11fb_merged-rep  wt13fb_merged-rep  wt15fb_merged-rep  \
0             Cdc25c           5.762959           5.314504           4.473968   
1              Ccnb1           8.281320           6.216438           6.447028   
2              Ccna2           8.151100           7.734222           6.719677   
3              Chek1           6.336196           5.375045           4.285237   
4               Cdk1           7.490175           7.144049           6.265231   

   wt18fb_merged-rep  wt11mb_merged-rep  wt13mb_merged-rep  wt15mb_merged-rep  \
0           3.019805           5.388667           4.056280           2.587823   
1           5.159121           8.279867           5.243044           4.713806   
2           5.634298           8.170345           6.717306           5.135032   
3           3.298304           6.173147           4.231317           3.209475   
4           4.852682           7.226526           6.014998           4.498720   

   wt18mb_merged-rep  wt11hb_merged-rep  wt13hb_merged-rep  wt15hb_merged-rep  \
0           2.239775           5.151138           2.655413           2.018563   
1           4.352169           7.914111           3.518844           4.124059   
2           4.868197           7.812570           5.025002           4.481259   
3           2.863345           5.813061           2.880232           2.735219   
4           4.076875           7.004874           4.610230           3.886277   

   wt18hb_merged-rep  wt11sc_merged-rep  wt13sc_merged-rep  wt15sc_merged-rep  \
0           1.964331           5.177371           2.996997           2.815994   
1           4.150267           7.902581           3.948672           4.929493   
2           4.624674           7.818953           5.424994           5.433449   
3           2.794969           5.974737           3.110397           3.782702   
4           4.011092           7.073529           5.085449           4.927312   

   wt18sc_merged-rep  
0           1.955338  
1           4.221396  
2           4.535896  
3           2.702454  
4           3.995343  
  external_gene_name  log2FoldChange_fb  log2FoldChange_mb  log2FoldChange_hb  \
0             Cdc25c          -1.845766          -0.930294          -0.380526   
1              Ccnb1          -1.421682          -0.769294          -0.362110   
2              Ccna2          -1.465808          -0.845869          -0.221654   
3              Chek1          -1.230909          -0.648620          -0.126571   
4               Cdk1          -1.245782          -0.496694           0.027641   

   log2FoldChange_sc  log2FoldChange_a11  log2FoldChange_a13  \
0          -0.507468           -0.280320           -1.331563   
1          -0.451948           -0.207963           -1.119941   
2          -0.468052           -0.151131           -1.237863   
3          -0.389211           -0.165957           -1.173925   
4          -0.387173           -0.188996           -0.940056   

   log2FoldChange_a15  log2FoldChange_a18  log2FoldChange_p11  \
0           -1.425839           -1.314303            0.083628   
1           -1.072435           -0.993573            0.016472   
2           -1.026113           -1.099205            0.044767   
3           -0.766666           -0.770994           -0.018204   
4           -0.925897           -0.641351            0.046495   

   log2FoldChange_p13  log2FoldChange_p15  log2FoldChange_p18  
0           -0.284097           -0.493841           -0.471433  
1           -0.257698           -0.434608           -0.432762  
2           -0.211759           -0.388392           -0.341808  
3            0.010150           -0.463843           -0.231386  
4           -0.009054           -0.266688           -0.173087  
  external_gene_name  ko11fb_merged-rep  ko13fb_merged-rep  ko15fb_merged-rep  \
0             Cdc25c           5.430465           3.825088           2.507804   
1              Ccnb1           8.204459           5.002764           4.907005   
2              Ccna2           8.113719           6.395736           5.160459   
3              Chek1           6.208641           4.020723           3.123135   
4               Cdk1           7.265407           6.047187           4.797252   

   ko18fb_merged-rep  ko11mb_merged-rep  ko13mb_merged-rep  ko15mb_merged-rep  \
0           1.680767           5.115939           2.981533           2.081817   
1           3.875634           7.898503           4.183626           4.166848   
2           4.299070           7.864624           5.506056           4.684449   
3           2.517492           5.912550           3.286192           2.971655   
4           3.942331           7.039619           5.157316           4.214915   

   ko18mb_merged-rep  ko11hb_merged-rep  ko13hb_merged-rep  ko15hb_merged-rep  \
0           1.561805           5.376289           2.388366           1.887962   
1           3.721016           8.110984           3.305844           3.921816   
2           4.034554           8.016841           4.874672           4.474250   
3           2.336929           5.975226           2.885219           2.752804   
4           3.788602           7.244248           4.731672           4.068549   

   ko18hb_merged-rep  ko11sc_merged-rep  ko13sc_merged-rep  ko15sc_merged-rep  \
0           1.474601           5.117148           2.626410           2.191761   
1           3.564282           7.741351           3.549144           4.364622   
2           4.136662           7.716354           5.002276           4.751888   
3           2.431686           5.788616           2.980105           2.972324   
4           3.765132           6.930466           4.789086           4.305096   

   ko18sc_merged-rep  
0           1.781626  
1           4.005966  
2           4.379974  
3           2.693746  
4           3.941685  
--------------------------------------------------------------------------------
                        pro-pro	Number of marked genes:	                        
--------------------------------------------------------------------------------
3 13
<ipython-input-28-31331348aa7a>:42: RuntimeWarning: Mean of empty slice
  h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
<ipython-input-28-31331348aa7a>:42: RuntimeWarning: invalid value encountered in greater
  h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
In [29]:
div_cmap = 'seismic' #'RdBu_r'
H3k_cols = [
 'forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal',
 'forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal',
 'forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal',
 'forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal',
            
 'midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal',
 'midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal',
 'midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal',
 'midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal',
            
 'hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal',
 'hindbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF435HPM_signal',
 'hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal',
 'hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal']

wt_cols = [c for c in df_all.columns if 'wt' in c and 'merged' in c]
ko_cols = [c for c in df_all.columns if 'ko' in c and 'merged' in c]
logfc = [c for c in df_all.columns if 'log2Fold' in c]

mean_h3k = np.nanmean(-1 * df_all[logfc].values, axis=1)
desc_sorted = (mean_h3k).argsort()  # Sort the genes by descending order
gene_names_sorted = df_all[gene_name].values[desc_sorted]

genes = ['Cdkn1a', 'Cdkn1b', 'Cdkn2a', 'Cdkn1c', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Rb1', 'E2f1', 'E2f2', 'E2f3', 'Cdk2', 'Cdk4', 'Cdk6']
genes2 = [] 
genes4 = [] 
genes3 = []

tst_lbl = 'anti-pro_even-h3k'
values = df_all[H3k_cols].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  H3k_cols, vae_data, vae_mse,
                  gene_name, mark='H3K27me3', method='input', 
                  title=f'WT HIST {tst_lbl} {experiment_name}', cmap=hist_cmap, 
                  genes=gene_names_sorted, values=values)

values = df_all[wt_cols].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  wt_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
                  method='input', title=f'WT {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

values = df_all[logfc].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  logfc, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=div_cmap,
                  method='input', title=f'LogFC {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

values = df_all[ko_cols].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  ko_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
                  method='input', title=f'KO {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

h3k_cols = [c for c in df_all if 'H3K27me3' in c and 'median' not in c and 'signal' in c and 'brain' in c]
h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
u = SciUtil()
u.dp([tst_lbl, 'Number of marked genes:'])
print(len(set(genes) & set(h3k_const_aff)), len(set(genes)))
  external_gene_name  \
0             Cdkn2a   
1             Cdkn2b   
2             Cdkn2c   
3              Ccnd1   
4             Cdkn1a   

   forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal  \
0                                           11.93962                    
1                                           17.47897                    
2                                            4.07843                    
3                                            2.60924                    
4                                            3.69001                    

   forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal  \
0                                            8.03731                    
1                                            5.40300                    
2                                            0.00000                    
3                                            3.31166                    
4                                            2.75775                    

   forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal  \
0                                            6.65861                    
1                                            3.56529                    
2                                            0.00000                    
3                                            3.98635                    
4                                            0.00000                    

   forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal  \
0                                            5.90222                    
1                                            4.07982                    
2                                            0.00000                    
3                                            4.36289                    
4                                            0.00000                    

   midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal  \
0                                           12.34218                   
1                                           12.34218                   
2                                            3.68897                   
3                                            2.98231                   
4                                            2.74551                   

   midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal  \
0                                            8.91941                   
1                                            6.99710                   
2                                            3.02577                   
3                                            4.47299                   
4                                            3.12033                   

   midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal  \
0                                            7.00107                   
1                                            5.97121                   
2                                            0.00000                   
3                                            4.51583                   
4                                            4.04349                   

   midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal  \
0                                            3.51227                   
1                                            3.62944                   
2                                            0.00000                   
3                                            3.13656                   
4                                            0.00000                   

   hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal  \
0                                           16.79157                    
1                                           16.79157                    
2                                            4.41238                    
3                                            3.30582                    
4                                            3.18672                    

   hindbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF435HPM_signal  \
0                                            8.48420                    
1                                            6.30277                    
2                                            2.79285                    
3                                            4.23949                    
4                                            2.54445                    

   hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal  \
0                                           11.49704                    
1                                           12.48229                    
2                                            5.18150                    
3                                            6.70107                    
4                                            0.00000                    

   hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal  
0                                            4.55180                   
1                                            4.97978                   
2                                            0.00000                   
3                                            4.41730                   
4                                            0.00000                   
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/matrix.py:1205: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. 
  self.fig.tight_layout(**tight_params)
  external_gene_name  wt11fb_merged-rep  wt13fb_merged-rep  wt15fb_merged-rep  \
0             Cdkn2a           0.081965           0.055547           0.163438   
1             Cdkn2b           0.081965           0.350432           0.413053   
2             Cdkn2c           2.795972           3.929023           3.936722   
3              Ccnd1           9.411207           8.575561           6.771200   
4             Cdkn1a           3.908049           4.656218           4.713217   

   wt18fb_merged-rep  wt11mb_merged-rep  wt13mb_merged-rep  wt15mb_merged-rep  \
0           0.171822           0.033312           0.070967           0.049808   
1           0.723561           0.071978           0.208532           0.523580   
2           3.197610           3.002832           2.874355           2.106403   
3           6.230616           9.440191           8.787826           6.684029   
4           5.040493           3.747754           4.591435           5.431125   

   wt18mb_merged-rep  wt11hb_merged-rep  wt13hb_merged-rep  wt15hb_merged-rep  \
0           0.093461           0.085673           0.054281           0.014650   
1           0.713752           0.904698           0.369921           0.374290   
2           2.271730           2.516919           1.587441           1.845625   
3           6.157662           9.123114           6.804078           5.681080   
4           6.344001           3.925292           4.424438           5.682118   

   wt18hb_merged-rep  wt11sc_merged-rep  wt13sc_merged-rep  wt15sc_merged-rep  \
0           0.081343           0.053746           0.037974           0.096582   
1           0.940972           0.329959           0.441814           0.658737   
2           2.568907           1.944132           1.793325           2.217030   
3           5.835546           8.934253           6.932590           6.524543   
4           6.241381           4.776748           4.154204           5.327085   

   wt18sc_merged-rep  
0           0.126187  
1           1.080613  
2           2.713289  
3           5.529480  
4           5.995773  
  external_gene_name  log2FoldChange_fb  log2FoldChange_mb  log2FoldChange_hb  \
0             Cdkn2a           8.198440           8.680234           9.364038   
1             Cdkn2b           4.714339           4.866714           4.822436   
2             Cdkn2c           0.110017           0.874814           1.128081   
3              Ccnd1           1.024355           0.475613           1.523859   
4             Cdkn1a           1.566764           0.549537           0.735342   

   log2FoldChange_sc  log2FoldChange_a11  log2FoldChange_a13  \
0           7.681831            7.625755            9.294939   
1           3.512037            4.349099            5.254427   
2           0.848435            0.969480            0.517564   
3           0.673827            0.196165           -0.356187   
4           0.733478            0.465161            1.465222   

   log2FoldChange_a15  log2FoldChange_a18  log2FoldChange_p11  \
0            7.952704            8.154555            7.221034   
1            4.802115            4.582345            1.099182   
2            0.465040            0.587984            1.375452   
3            1.061552            1.617141            0.339446   
4            0.798905            1.023042            0.279383   

   log2FoldChange_p13  log2FoldChange_p15  log2FoldChange_p18  
0            8.982836            8.505726            8.211635  
1            4.373813            4.582029            3.710329  
2            1.435444            1.090585            0.566230  
3            0.539246            1.092288            1.771155  
4            1.163413            0.452229            0.696539  
  external_gene_name  ko11fb_merged-rep  ko13fb_merged-rep  ko15fb_merged-rep  \
0             Cdkn2a           3.228548           5.254777           4.544105   
1             Cdkn2b           1.115676           3.146196           3.518678   
2             Cdkn2c           4.031161           4.373202           3.684748   
3              Ccnd1           9.725485           8.594899           8.116702   
4             Cdkn1a           4.395186           6.475518           6.083869   

   ko18fb_merged-rep  ko11mb_merged-rep  ko13mb_merged-rep  ko15mb_merged-rep  \
0           4.884756           2.923626           4.524850           4.166973   
1           3.996336           0.996446           3.166510           3.585624   
2           3.407147           3.511233           3.273202           3.146505   
3           7.966551           9.472000           7.954697           7.447099   
4           6.592410           4.080616           5.514889           5.684844   

   ko18mb_merged-rep  ko11hb_merged-rep  ko13hb_merged-rep  ko15hb_merged-rep  \
0           4.697869           3.470524           4.287647           4.185092   
1           4.128003           1.328320           3.224278           3.783847   
2           3.082391           3.820127           2.749030           2.870688   
3           7.588084           9.531941           7.618633           7.488938   
4           6.805126           4.627656           5.528786           6.074460   

   ko18hb_merged-rep  ko11sc_merged-rep  ko13sc_merged-rep  ko15sc_merged-rep  \
0           4.857104           2.219755           3.732213           3.541950   
1           4.263627           0.803604           2.518129           3.144523   
2           3.084977           2.910008           2.731408           2.959750   
3           7.712941           9.226480           7.025576           6.940598   
4           6.933814           4.588550           5.127861           5.801270   

   ko18sc_merged-rep  
0           4.204403  
1           3.449436  
2           3.204036  
3           7.159267  
4           6.727258  
--------------------------------------------------------------------------------
                   anti-pro_even-h3k	Number of marked genes:	                   
--------------------------------------------------------------------------------
18 19
<ipython-input-29-32bfe5fcf971>:61: RuntimeWarning: Mean of empty slice
  h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
<ipython-input-29-32bfe5fcf971>:61: RuntimeWarning: invalid value encountered in greater
  h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
In [30]:
# Sort the genes by average H3K27me3
mean_h3k = np.nanmean(df_all[logfc].values, axis=1)
desc_sorted = (mean_h3k).argsort()  # Sort the genes by descending order
gene_names_sorted = df_all[gene_name].values[desc_sorted]

genes = ['Ccna1', 'Ccna2', 'Ccnb1', 'Ccnb2', 'Ccnb3', 'Cdc25a', 'Cdc25b', 'Cdc25c', 'Cdk1', 'Chek1', 'Chek2', 'Wee1', 'Wee2']
genes2 = [] 
genes4 = [] 
genes3 = []

tst_lbl = 'pro-pro_even-h3k'
values = df_all[H3k_cols].values[desc_sorted]

plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  H3k_cols, vae_data, vae_mse,
                  gene_name, mark='H3K27me3', method='input', 
                  title=f'WT HIST {tst_lbl} {experiment_name}', cmap=hist_cmap, 
                  genes=gene_names_sorted, values=values)

values = df_all[wt_cols].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  wt_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
                  method='input', title=f'WT {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

values = df_all[logfc].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  logfc, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=div_cmap,
                  method='input', title=f'LogFC {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

values = df_all[ko_cols].values[desc_sorted]
plot_gene_heatmap(df_all, genes, genes2, genes4, genes3,
                  ko_cols, all_vae_data, vae_mse,
                  gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
                  method='input', title=f'KO {tst_lbl} {experiment_name}', 
                  genes=gene_names_sorted, values=values)

h3k_cols = [c for c in df_all if 'H3K27me3' in c and 'median' not in c and 'signal' in c and 'brain' in c]
h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
u = SciUtil()
u.dp([tst_lbl, 'Number of marked genes:'])
print(len(set(genes) & set(h3k_const_aff)), len(set(genes)))
  external_gene_name  \
0             Cdc25c   
1              Ccnb1   
2              Ccna2   
3              Chek1   
4               Cdk1   

   forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal  \
0                                                0.0                   
1                                                0.0                   
2                                                0.0                   
3                                                0.0                   
4                                                0.0                   

   midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal  \
0                                                0.0                   
1                                                0.0                   
2                                                0.0                   
3                                                0.0                   
4                                                0.0                   

   midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal  \
0                                                0.0                   
1                                                0.0                   
2                                                0.0                   
3                                                0.0                   
4                                                0.0                   

   midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal  \
0                                                0.0                   
1                                                0.0                   
2                                                0.0                   
3                                                0.0                   
4                                                0.0                   

   hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   hindbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF435HPM_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal  \
0                                                0.0                    
1                                                0.0                    
2                                                0.0                    
3                                                0.0                    
4                                                0.0                    

   hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal  
0                                                0.0                   
1                                                0.0                   
2                                                0.0                   
3                                                0.0                   
4                                                0.0                   
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/matrix.py:1205: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. 
  self.fig.tight_layout(**tight_params)
  external_gene_name  wt11fb_merged-rep  wt13fb_merged-rep  wt15fb_merged-rep  \
0             Cdc25c           5.762959           5.314504           4.473968   
1              Ccnb1           8.281320           6.216438           6.447028   
2              Ccna2           8.151100           7.734222           6.719677   
3              Chek1           6.336196           5.375045           4.285237   
4               Cdk1           7.490175           7.144049           6.265231   

   wt18fb_merged-rep  wt11mb_merged-rep  wt13mb_merged-rep  wt15mb_merged-rep  \
0           3.019805           5.388667           4.056280           2.587823   
1           5.159121           8.279867           5.243044           4.713806   
2           5.634298           8.170345           6.717306           5.135032   
3           3.298304           6.173147           4.231317           3.209475   
4           4.852682           7.226526           6.014998           4.498720   

   wt18mb_merged-rep  wt11hb_merged-rep  wt13hb_merged-rep  wt15hb_merged-rep  \
0           2.239775           5.151138           2.655413           2.018563   
1           4.352169           7.914111           3.518844           4.124059   
2           4.868197           7.812570           5.025002           4.481259   
3           2.863345           5.813061           2.880232           2.735219   
4           4.076875           7.004874           4.610230           3.886277   

   wt18hb_merged-rep  wt11sc_merged-rep  wt13sc_merged-rep  wt15sc_merged-rep  \
0           1.964331           5.177371           2.996997           2.815994   
1           4.150267           7.902581           3.948672           4.929493   
2           4.624674           7.818953           5.424994           5.433449   
3           2.794969           5.974737           3.110397           3.782702   
4           4.011092           7.073529           5.085449           4.927312   

   wt18sc_merged-rep  
0           1.955338  
1           4.221396  
2           4.535896  
3           2.702454  
4           3.995343  
  external_gene_name  log2FoldChange_fb  log2FoldChange_mb  log2FoldChange_hb  \
0             Cdc25c          -1.845766          -0.930294          -0.380526   
1              Ccnb1          -1.421682          -0.769294          -0.362110   
2              Ccna2          -1.465808          -0.845869          -0.221654   
3              Chek1          -1.230909          -0.648620          -0.126571   
4               Cdk1          -1.245782          -0.496694           0.027641   

   log2FoldChange_sc  log2FoldChange_a11  log2FoldChange_a13  \
0          -0.507468           -0.280320           -1.331563   
1          -0.451948           -0.207963           -1.119941   
2          -0.468052           -0.151131           -1.237863   
3          -0.389211           -0.165957           -1.173925   
4          -0.387173           -0.188996           -0.940056   

   log2FoldChange_a15  log2FoldChange_a18  log2FoldChange_p11  \
0           -1.425839           -1.314303            0.083628   
1           -1.072435           -0.993573            0.016472   
2           -1.026113           -1.099205            0.044767   
3           -0.766666           -0.770994           -0.018204   
4           -0.925897           -0.641351            0.046495   

   log2FoldChange_p13  log2FoldChange_p15  log2FoldChange_p18  
0           -0.284097           -0.493841           -0.471433  
1           -0.257698           -0.434608           -0.432762  
2           -0.211759           -0.388392           -0.341808  
3            0.010150           -0.463843           -0.231386  
4           -0.009054           -0.266688           -0.173087  
  external_gene_name  ko11fb_merged-rep  ko13fb_merged-rep  ko15fb_merged-rep  \
0             Cdc25c           5.430465           3.825088           2.507804   
1              Ccnb1           8.204459           5.002764           4.907005   
2              Ccna2           8.113719           6.395736           5.160459   
3              Chek1           6.208641           4.020723           3.123135   
4               Cdk1           7.265407           6.047187           4.797252   

   ko18fb_merged-rep  ko11mb_merged-rep  ko13mb_merged-rep  ko15mb_merged-rep  \
0           1.680767           5.115939           2.981533           2.081817   
1           3.875634           7.898503           4.183626           4.166848   
2           4.299070           7.864624           5.506056           4.684449   
3           2.517492           5.912550           3.286192           2.971655   
4           3.942331           7.039619           5.157316           4.214915   

   ko18mb_merged-rep  ko11hb_merged-rep  ko13hb_merged-rep  ko15hb_merged-rep  \
0           1.561805           5.376289           2.388366           1.887962   
1           3.721016           8.110984           3.305844           3.921816   
2           4.034554           8.016841           4.874672           4.474250   
3           2.336929           5.975226           2.885219           2.752804   
4           3.788602           7.244248           4.731672           4.068549   

   ko18hb_merged-rep  ko11sc_merged-rep  ko13sc_merged-rep  ko15sc_merged-rep  \
0           1.474601           5.117148           2.626410           2.191761   
1           3.564282           7.741351           3.549144           4.364622   
2           4.136662           7.716354           5.002276           4.751888   
3           2.431686           5.788616           2.980105           2.972324   
4           3.765132           6.930466           4.789086           4.305096   

   ko18sc_merged-rep  
0           1.781626  
1           4.005966  
2           4.379974  
3           2.693746  
4           3.941685  
--------------------------------------------------------------------------------
                   pro-pro_even-h3k	Number of marked genes:	                    
--------------------------------------------------------------------------------
3 13
<ipython-input-30-9196e29897fa>:42: RuntimeWarning: Mean of empty slice
  h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
<ipython-input-30-9196e29897fa>:42: RuntimeWarning: invalid value encountered in greater
  h3k_const_aff = df_all[np.nanmean(df_all[h3k_cols].values, axis=1) > 0][gene_name].values
In [31]:
df_sig = pd.read_csv(f'{input_dir}df-significant_epi-2500_20210124.csv')

mean_h3k = np.nanmax(-1 * df_sig[logfc].values, axis=1)
log_fc = df_sig[logfc].values
desc_sorted = (mean_h3k).argsort()  # Sort the genes by descending order
mean_h3k = mean_h3k[desc_sorted]
gene_names_sorted = df_sig[gene_name].values[desc_sorted]
h3k = np.nanmedian(df_sig[h3k_cols].values, axis=1)[desc_sorted]

genes = ['Ccna1', 'Ccna2', 'Ccnb1', 'Ccnb2', 'Ccnb3', 'Cdc25a', 'Cdc25b', 'Cdc25c', 'Cdk1', 'Chek1', 'Chek2', 'Wee1', 'Wee2']

genes = ['Cdkn1a', 'Cdkn1b', 'Cdkn2a', 'Cdkn1c', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Rb1', 'E2f1', 'E2f2', 'E2f3', 'Cdk2', 'Cdk4', 'Cdk6']
for i, g in enumerate(gene_names_sorted):
    if g in genes:
        print(g, mean_h3k[i], h3k[i])
        
Cdkn2a -7.221033945514599 8.03731
Cdkn2b -1.0991824382536 5.71146
Cdkn1a -0.279382582204216 3.04388
Cdkn2c -0.110017150923737 3.42122
Cdk6 0.17687115073842 3.39135
Rb1 0.28509345889742 2.27189
Ccnd1 0.35618749389916704 3.49275
Cdkn2d 0.362534200560768 nan
Ccnd3 0.389225596452286 2.66132
Cdkn1c 0.7476729958524408 3.74455
Cdkn1b 0.8321717646928408 3.30582
Ccnd2 0.8398076176407309 4.2266
E2f3 0.88058583715709 2.28051
Cdk2 0.885401027504756 2.83147
Ccne2 0.8909330290236829 2.41045
Ccne1 0.935647186932546 3.14122
Cdk4 1.04599959370572 3.08799
E2f1 1.50217550847294 2.50881
E2f2 1.57193579459995 2.75342
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3062: DtypeWarning: Columns (4) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,
In [32]:
###############################################################################
#                                                                             #
#    This program is free software: you can redistribute it and/or modify     #
#    it under the terms of the GNU General Public License as published by     #
#    the Free Software Foundation, either version 3 of the License, or        #
#    (at your option) any later version.                                      #
#                                                                             #
#    This program is distributed in the hope that it will be useful,          #
#    but WITHOUT ANY WARRANTY; without even the implied warranty of           #
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the            #
#    GNU General Public License for more details.                             #
#                                                                             #
#    You should have received a copy of the GNU General Public License        #
#    along with this program. If not, see <http://www.gnu.org/licenses/>.     #
#                                                                             #
###############################################################################

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

from sciviso import Vis


class Violinplot(Vis):

    def __init__(self, df: pd.DataFrame, x: object, y: object, title='', xlabel='', ylabel='', hue=None, order=None,
                 hue_order=None, showfliers=False, add_dots=False, figsize=(1.5, 1.5), title_font_size=8,
                 add_stats=False, stat_method='Mann-Whitney',
                 label_font_size=6, title_font_weight=700):
        super().__init__(df, figsize=figsize, title_font_size=title_font_size, label_font_size=label_font_size,
                         title_font_weight=title_font_weight)
        self.df = df
        self.x = x
        self.y = y
        self.xlabel = xlabel
        self.ylabel = ylabel
        self.title = title
        self.hue = hue
        self.order = order
        self.hue_order = hue_order
        self.showfliers = showfliers
        self.add_dots = add_dots
        self.add_stats = add_stats
        self.stat_method = stat_method

    def plot(self):
        x, y, hue, order, hue_order = self.x, self.y, self.hue, self.order, self.hue_order
        if not isinstance(self.x, str) and not isinstance(self.y, str):
            vis_df = pd.DataFrame()
            vis_df['x'] = x
            vis_df['y'] = y
            x = 'x'
            y = 'y'
            if self.hue is not None:
                vis_df['colour'] = self.hue
                hue = 'colour'
            if order is None:
                order = list(set(vis_df['x'].values))
                order.sort()
        else:
            vis_df = self.df
        # set the orders
        if hue_order is None and hue is not None:
            hue_order = list(set(vis_df[hue].values))
            hue_order.sort()
        if order is None:
            order = list(set(vis_df[x].values))
            order.sort()

        ax = sns.violinplot(data=vis_df, x=x, y=y, hue=hue, hue_order=hue_order, order=order, palette=self.palette,
                            showfliers=self.showfliers, inner="quartile")
        if self.add_dots:
            ax = sns.stripplot(data=vis_df, x=x, y=y, hue_order=hue_order, order=order, color='.2')

        if self.add_stats:
            # Add all pairs in the order if the box pairs is none

            pairs = []
            box_pairs = []
            for i in order:
                for j in order:
                    if i != j:
                        # Ensure we don't get duplicates
                        pair = f'{i}{j}' if i < j else f'{j}{i}'
                        if pair not in pairs:
                            box_pairs.append((i, j))
                            pairs.append(pair)
            # Add stats annotation
            add_stat_annotation(ax, data=vis_df, x=x, y=y, order=order,
                                box_pairs=box_pairs,
                                test=self.stat_method, text_format='star', loc='inside', verbose=2)
        ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')
        ax.tick_params(labelsize=self.label_font_size)
        plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=self.label_font_size)
        self.add_labels()
        self.set_ax_params(ax)
        return ax

Plot human relationships

Immune genes

genes = ['Gbp2','Gbp4','Gbp6','Gbp8','Gbp9','Gmnn','H2−D','Mb1','Hmgb2','Ifi204','Ifi27l2a','Ifi47','Ifit3','Ifit3b', 'Clec10a','Clec4a2','Clec4n','Clec5a','Csf1r','Csf3' ,'C1qb','C1ra','C2','C3ar1','C4b','C5ar1','Car3','Card11','Ccdc80','Ccl12','Ccl2','Ccl3','Ccl6']

genes = ['Aspm','Aunip','Aurkb','Bub1b','Kif14','Kif15','Kif18a','Kif18b','Kif22','Kif2c','Knl1','Knt']

tissue_genes = ['Nr2e1', 'Fezf2', 'Helt', 'Gsx2', 'Emx1', 'Tfap2c', 'Eomes', 'Ptgds', 'Ttr', 'Grin2d', 'Aqp4', 'Hoxb8']

Map between human and mouse

i = 0
count = 0
human_mouse_dict = {}
mouse_human_dict = {}
for g in results_NN['ensembl_gene_id'].values:
    if mmusculus_homolog_ensembl_gene[i] != None:
        if similarity[i] is not None and float(similarity[i]) > 80:  ## Only keep them if the similarlity is > 0.5
            human_mouse_dict[g] =  mmusculus_homolog_ensembl_gene[i]
            mouse_human_dict[mmusculus_homolog_ensembl_gene[i]] = g
            count += 1
    i += 1

len(mouse_human_dict)

human_mouse_values = []
mouse_human_dict = {}
gene_names = df_all[gene_name].values
i = 0
for g in df_all['ensembl_gene_id'].values:
    human_mouse_values.append(human_mouse_dict.get(g))
    if human_mouse_dict.get(g):
        mouse_human_dict[human_mouse_dict.get(g)] = gene_names[i]
    i += 1
# hu_c = ['ENSG00000131747', 'ENSG00000164104', 'ENSG00000142945', 'ENSG00000175063', 'ENSG00000134057', 'ENSG00000145386', 'ENSG00000079616', 'ENSG00000117399']

annot_df = pd.read_csv(os.path.join(supp_dir, 'mm10Sorted_mmusculus_gene_ensembl-GRCm38.p6.csv'))
gene_names = annot_df[gene_name].values
gene_name_to_ens_id = {}
for i, g in enumerate(annot_df['ensembl_gene_id'].values):
    if not gene_name_to_ens_id.get(gene_names[i]):
        gene_name_to_ens_id[gene_names[i]] = g


genes = df_sig[gene_name].values
human_genes = []
mm_grps = []
for g in genes:
    ens = gene_name_to_ens_id.get(g)
    if ens is not None:
        # Now try get human
        hu = mouse_human_dict.get(ens)
        if hu:
            human_genes.append(hu)
            mm_grps.append(g)
print(len(genes), len(human_genes))


# Cell cycle genes
cell_cycle_g = pd.read_csv(f'{supp_dir}41422_2016_BFcr201684_MOESM25_ESM.csv')
cell_cycle_g = cell_cycle_g[cell_cycle_g['Core 67'] == 'Yes']
cell_cycle_g1 = cell_cycle_g[cell_cycle_g['Phase '] == 'G1-S']
cell_cycle_m = cell_cycle_g[cell_cycle_g['Phase '] == 'G2-M']
m_genes = cell_cycle_m['ENSG'].values
for i, hu in enumerate(hu_grps):
    mmg = []
    for j, g in enumerate(hu):
        if g in m_genes:
            mmg.append(mm_n_grps[i][j])
    print(i, set(hu)&set(m_genes), len(set(hu)&set(m_genes)), len(m_genes))
    print(', '.join(mmg))
c1_genes = cell_cycle_g1['ENSG'].values
for i, hu in enumerate(hu_grps):
    print(i, set(hu)&set(c1_genes), len(set(hu)&set(c1_genes)), len(c1_genes))

# Plot the heatmaps of the human expression
ofc_cols = [c for c in human_emb_a if 'MD' in c]
plot_gene_heatmap(human_emb_a, tgs_hu, [], [], [],
                  ofc_cols, all_vae_data, vae_mse,
                  'ensembl_gene_id', mark='H3K27me3', merge_reps=False,  cmap=rna_cmap,
                  method='input', title=f'Mid RNA WT human {experiment_name}')
ofc_cols = [c for c in human_emb_a if 'OFC' in c]
plot_gene_heatmap(human_emb_a, tgs_hu, [], [], [],
                  ofc_cols, all_vae_data, vae_mse,
                  'ensembl_gene_id', mark='H3K27me3', merge_reps=False,  cmap=rna_cmap,
                  method='input', title=f'FB RNA WT human {experiment_name}')
ofc_cols = [c for c in human_emb_a if 'CBC' in c]
plot_gene_heatmap(human_emb_a, tgs_hu, [], [], [],
                  ofc_cols, all_vae_data, vae_mse,
                  'ensembl_gene_id', mark='H3K27me3', merge_reps=False,  cmap=rna_cmap,
                  method='input', title=f'HB RNA WT human {experiment_name}')

len(set(human_genes)&set(m_genes))
In [33]:
# Now we want to match the genes in each of our groups, via ensembl ID to human gene name 
# So we can look at how they expressed in human embryos
# Map from gene in each "gene group" to human ensembl ID
# -----------------------------------------------------------------------------------
#                         Save genes in each of the clusters we annotated (ORA)
# -----------------------------------------------------------------------------------
# from scibiomart import SciBiomart

# sb = SciBiomart()
# sb.set_mart('ENSEMBL_MART_ENSEMBL')
# sb.set_dataset('hsapiens_gene_ensembl')
# attributes = ['ensembl_gene_id', 'mmusculus_homolog_ensembl_gene', 'mmusculus_homolog_perc_id_r1']
# results = sb.run_query({}, attributes)

# # Make a list of the genes that map
# human_mouse_values = []
# mouse_human_dict = {}

# # Filter the results to include only those that have Not Nones in they're mmusculus_homolog_ensembl_gene
# results_NN = results[results['mmusculus_homolog_ensembl_gene'] != None]
# mmusculus_homolog_ensembl_gene = results_NN['mmusculus_homolog_ensembl_gene'].values
# similarity = results_NN['mmusculus_homolog_perc_id_r1'].values

from scibiomart import SciBiomart

annot_df = pd.read_csv(os.path.join(supp_dir, 'mm10Sorted_mmusculus_gene_ensembl-GRCm38.p6.csv'))
gene_names = annot_df[gene_name].values
gene_name_to_ens_id = {}
for i, g in enumerate(annot_df['ensembl_gene_id'].values):
    if not gene_name_to_ens_id.get(gene_names[i]):
        gene_name_to_ens_id[gene_names[i]] = g
        

sb = SciBiomart()
sb.set_mart('ENSEMBL_MART_ENSEMBL')
sb.set_dataset('hsapiens_gene_ensembl')
attributes = ['ensembl_gene_id', 'mmusculus_homolog_ensembl_gene', 'mmusculus_homolog_perc_id_r1']
results = sb.run_query({}, attributes)

# Make a list of the genes that map
human_mouse_values = []
mouse_human_dict = {}

# Filter the results to include only those that have Not Nones in they're mmusculus_homolog_ensembl_gene
results_NN = results[results['mmusculus_homolog_ensembl_gene'] != None]
mmusculus_homolog_ensembl_gene = results_NN['mmusculus_homolog_ensembl_gene'].values
similarity = results_NN['mmusculus_homolog_perc_id_r1'].values

i = 0
count = 0
human_mouse_dict = {}
mouse_human_dict = {}
for g in results_NN['ensembl_gene_id'].values:
    if mmusculus_homolog_ensembl_gene[i] != None:
        if similarity[i] is not None and float(similarity[i]) > 80:  ## Only keep them if the similarlity is > 0.5
            human_mouse_dict[g] =  mmusculus_homolog_ensembl_gene[i]
            mouse_human_dict[mmusculus_homolog_ensembl_gene[i]] = g
            count += 1
    i += 1

len(mouse_human_dict)
genes = df_all[gene_name].values
human_genes = []
mm_grps = []
for g in genes:
    ens = gene_name_to_ens_id.get(g)
    if ens is not None:
        # Now try get human
        hu = mouse_human_dict.get(ens)
        if hu:
            human_genes.append(hu)
            mm_grps.append(g)
print(len(genes), len(human_genes))

hu_grps = []
mm_n_grps = []

for i in range(0, n_clusters):
    genes = df[gene_name].values[vae_set_idxs[i]]
    human_genes = []
    mm_grps = []
    for g in genes:
        ens = gene_name_to_ens_id.get(g)
        if ens is not None:
            # Now try get human
            hu = mouse_human_dict.get(ens)
            if hu:
                human_genes.append(hu)
                mm_grps.append(g)
    print(len(genes), len(human_genes))
    hu_grps.append(human_genes)

    mm_n_grps.append(mm_grps)
    
20900 11385
62 23
180 124
80 62
223 71
156 122
121 77
In [117]:
from statannot import add_stat_annotation

human_b = pd.read_csv(supp_dir + 'genes_matrix_csv/expression_matrix.csv', header=None)
human_rm = pd.read_csv(supp_dir + 'genes_matrix_csv/rows_metadata.csv')
human_cm = pd.read_csv(supp_dir + 'genes_matrix_csv/columns_metadata.csv')

# Great now find these in our embryonic and plot box plots
hu_idxs = [[], [], [], [], [], []]
for i, g in enumerate(human_rm['ensembl_gene_id'].values):
    for gi, genes in enumerate(hu_grps):
        if g in genes:
            hu_idxs[gi].append(i)
            break

ages = human_cm['age'].values
gender = human_cm['gender'].values
regions = human_cm['structure_acronym'].values
col_names = ['row-id']
donor_id = human_cm['donor_id'].values

# Rename columns to early, mid, w1 = e8, 9, w2 = 12, 13, w3 = 16, 17, w4 = > 17
for i, r in enumerate(regions):
    a = int(ages[i].split(' ')[0])
    stage = 0
    if a > 17:
        stage = 'W4'
    elif a > 13:
        stage = 'W3'
    elif a > 9:
        stage = 'W2'
    else:
        stage = 'W1'
    
    cn = regions[i] + '_' + ages[i].replace(' ', '-') + '_' + stage + '_' + gender[i]  + '_' + str(donor_id[i])
    col_names.append(cn)

human_b.columns = col_names
tissue_c = ['MFC', 'OFC', 'DFC', 'NFC', 'M1C', 'S1C', 'IPC', 'A1C', 'STC', 'ITC', 'V1C', 'HIP', 'AMY', 'STR', 'MD', 'CBC']
human_emb = human_b[[c for c in human_b.columns if 'pcw' in c and ('OFC' in c or 'MD' in c or 'CBC' in c or
                                                                  'MFC' in c or 'DFC' in c or 'M1C' in c or
                                                                  'S1C' in c or 'IPC' in c or 'A1C' in c or
                                                                  'STC' in c or 'V1C' in c or 'HIP' in c or
                                                                  'HIP' in c or 'AMY' in c or 'STR' in c)]]

human_emb['ensembl_gene_id'] = human_rm['ensembl_gene_id'].values
human_emb['gene_symbol'] = human_rm['gene_symbol'].values
human_emb['entrez_id'] = human_rm['entrez_id'].values

human_emb_a = pd.DataFrame()
human_emb_num = pd.DataFrame()
human_emb_a['ensembl_gene_id'] = human_rm['ensembl_gene_id'].values
human_emb = human_emb.fillna(0)
for c in human_emb.columns:
    if 'entrez_id' not in c:
        try:
            human_emb_a[c] = np.log2(human_emb[c].values + 1)
            human_emb_num[c] = np.log2(human_emb[c].values + 1)
        except:
            print(c)
    
    
def plot_hu_gene_boxplot(df, title, cluster_id, cols, idxs, ylim=10):
    idxs = idxs
    boxplot = Boxplot(df, 'ensembl_gene_id', cols[0])
    box_df = boxplot.format_data_for_boxplot(df, cols, 'ensembl_gene_id', df['ensembl_gene_id'].values[idxs])
    print(box_df)
    boxplot = Boxplot(box_df, "Conditions", "Values", add_stats=False, add_dots=False, figsize=(3.5,3), 
                       order=cols, title_font_size=12, label_font_size=10)
    boxplot.palette = 'Greys'

    ax = boxplot.plot()
    plt.title(title)
    ax.set_ylim(0, ylim)

    save_fig(f'{title}')
    
    plt.show()

for i, gr_i in enumerate(hu_idxs):
    for r in tissue_c:
        try:
            cs = [c for c in human_emb_a if r in c]
            print(gr_i)
            print(cs)
            ylim = 10
            if i == 1:
                ylim = 10
            elif i == 2:
                ylim = 3
            elif i == 3:
                ylim = 8
            elif i == 4:
                ylim = 5
            elif i == 5:
                ylim = 4
            print(len(gr_i))
            plot_hu_gene_boxplot(human_emb_a, f'Cluster {i} {r}', i, cs, gr_i, ylim)
        except:
            u.err_p([r])


""" PCA of the human embryo samples. """

pca_vae = PCA(n_components=2)  # make sure we have the same components as the VAE to keep the test fair
pca_values = pca_vae.fit_transform(np.log2(human_emb_num.values.T + 1))

reg_cols = []
t_col = []
g_col = []
for c in human_emb_num.columns:
    if 'OFC' in c:
        reg_cols.append(fb_colour)
    elif 'MD' in c:
        reg_cols.append(mb_colour)
    elif 'CBC' in c:
        reg_cols.append(hb_colour)
    elif 'HIP' in c:
        reg_cols.append('blue')
    elif 'AMY' in c:
        reg_cols.append('green')
    elif 'STR' in c:
        reg_cols.append('purple')
    else:
        reg_cols.append('grey')
    tc = int(c.split('_')[1].split('-')[0])
    if tc < 12:
        t_col.append(e11_colour)
    elif tc < 16:
        t_col.append(e13_colour) 
    elif tc < 22:
        t_col.append(e15_colour) 
    else:
        t_col.append(e18_colour) 
    if '_F' in c:
        g_col.append('pink')
    else:
        g_col.append('blue')
        
# Plot the scatter
plt.scatter(pca_values[:,0], pca_values[:,1], c=reg_cols)
plt.show()

plt.scatter(pca_values[:,0], pca_values[:,1], c=t_col)
plt.show()

plt.scatter(pca_values[:,0], pca_values[:,1], c=g_col)
plt.show()


OFC_cols = [c for c in human_emb.columns if 'OFC' in c]
g2m_genes = ['ENSG00000131747', 'ENSG00000164104', 'ENSG00000142945', 'ENSG00000175063', 'ENSG00000134057', 'ENSG00000145386', 'ENSG00000079616', 'ENSG00000117399']
<ipython-input-117-0f097d683b9a>:45: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  human_emb['ensembl_gene_id'] = human_rm['ensembl_gene_id'].values
<ipython-input-117-0f097d683b9a>:46: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  human_emb['gene_symbol'] = human_rm['gene_symbol'].values
<ipython-input-117-0f097d683b9a>:47: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  human_emb['entrez_id'] = human_rm['entrez_id'].values
ensembl_gene_id
gene_symbol
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['MFC_8-pcw_W1_M_13058', 'MFC_9-pcw_W1_M_12833', 'MFC_12-pcw_W2_F_12835', 'MFC_12-pcw_W2_F_13060', 'MFC_13-pcw_W2_M_12820', 'MFC_13-pcw_W2_F_12834', 'MFC_13-pcw_W2_M_12888', 'MFC_16-pcw_W3_M_12287', 'MFC_16-pcw_W3_M_12837', 'MFC_16-pcw_W3_M_12879', 'MFC_17-pcw_W3_F_12880', 'MFC_19-pcw_W4_F_12885', 'MFC_21-pcw_W4_M_12886', 'MFC_24-pcw_W4_M_12288', 'MFC_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples  Values                 Conditions
0         MFC_8-pcw_W1_M_13058     0.0       MFC_8-pcw_W1_M_13058
1         MFC_9-pcw_W1_M_12833     0.0       MFC_9-pcw_W1_M_12833
2        MFC_12-pcw_W2_F_12835     0.0      MFC_12-pcw_W2_F_12835
3        MFC_12-pcw_W2_F_13060     0.0      MFC_12-pcw_W2_F_13060
4        MFC_13-pcw_W2_M_12820     0.0      MFC_13-pcw_W2_M_12820
..                         ...     ...                        ...
340      MFC_17-pcw_W3_F_12880     0.0      MFC_17-pcw_W3_F_12880
341      MFC_19-pcw_W4_F_12885     0.0      MFC_19-pcw_W4_F_12885
342      MFC_21-pcw_W4_M_12886     0.0      MFC_21-pcw_W4_M_12886
343      MFC_24-pcw_W4_M_12288     0.0      MFC_24-pcw_W4_M_12288
344  MFC_37-pcw_W4_M_263195015     0.0  MFC_37-pcw_W4_M_263195015

[345 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['OFC_8-pcw_W1_M_13058', 'OFC_9-pcw_W1_M_12833', 'OFC_12-pcw_W2_F_12835', 'OFC_12-pcw_W2_F_12960', 'OFC_12-pcw_W2_F_13060', 'OFC_13-pcw_W2_M_12820', 'OFC_13-pcw_W2_F_12834', 'OFC_13-pcw_W2_M_12888', 'OFC_16-pcw_W3_M_12287', 'OFC_16-pcw_W3_M_12837', 'OFC_17-pcw_W3_F_12880', 'OFC_21-pcw_W4_M_12886', 'OFC_24-pcw_W4_M_12288', 'OFC_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples  Values                 Conditions
0         OFC_8-pcw_W1_M_13058     0.0       OFC_8-pcw_W1_M_13058
1         OFC_9-pcw_W1_M_12833     0.0       OFC_9-pcw_W1_M_12833
2        OFC_12-pcw_W2_F_12835     0.0      OFC_12-pcw_W2_F_12835
3        OFC_12-pcw_W2_F_12960     0.0      OFC_12-pcw_W2_F_12960
4        OFC_12-pcw_W2_F_13060     0.0      OFC_12-pcw_W2_F_13060
..                         ...     ...                        ...
317      OFC_16-pcw_W3_M_12837     0.0      OFC_16-pcw_W3_M_12837
318      OFC_17-pcw_W3_F_12880     0.0      OFC_17-pcw_W3_F_12880
319      OFC_21-pcw_W4_M_12886     0.0      OFC_21-pcw_W4_M_12886
320      OFC_24-pcw_W4_M_12288     0.0      OFC_24-pcw_W4_M_12288
321  OFC_37-pcw_W4_M_263195015     0.0  OFC_37-pcw_W4_M_263195015

[322 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['DFC_8-pcw_W1_M_13058', 'DFC_9-pcw_W1_M_12833', 'DFC_12-pcw_W2_F_12835', 'DFC_12-pcw_W2_F_12960', 'DFC_12-pcw_W2_F_13060', 'DFC_13-pcw_W2_M_12820', 'DFC_13-pcw_W2_F_12834', 'DFC_13-pcw_W2_M_12888', 'DFC_16-pcw_W3_M_12287', 'DFC_16-pcw_W3_M_12837', 'DFC_16-pcw_W3_M_12879', 'DFC_17-pcw_W3_F_12880', 'DFC_19-pcw_W4_F_12885', 'DFC_21-pcw_W4_M_12886', 'DFC_24-pcw_W4_M_12288', 'DFC_26-pcw_W4_F_12949', 'DFC_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples  Values                 Conditions
0         DFC_8-pcw_W1_M_13058     0.0       DFC_8-pcw_W1_M_13058
1         DFC_9-pcw_W1_M_12833     0.0       DFC_9-pcw_W1_M_12833
2        DFC_12-pcw_W2_F_12835     0.0      DFC_12-pcw_W2_F_12835
3        DFC_12-pcw_W2_F_12960     0.0      DFC_12-pcw_W2_F_12960
4        DFC_12-pcw_W2_F_13060     0.0      DFC_12-pcw_W2_F_13060
..                         ...     ...                        ...
386      DFC_19-pcw_W4_F_12885     0.0      DFC_19-pcw_W4_F_12885
387      DFC_21-pcw_W4_M_12886     0.0      DFC_21-pcw_W4_M_12886
388      DFC_24-pcw_W4_M_12288     0.0      DFC_24-pcw_W4_M_12288
389      DFC_26-pcw_W4_F_12949     0.0      DFC_26-pcw_W4_F_12949
390  DFC_37-pcw_W4_M_263195015     0.0  DFC_37-pcw_W4_M_263195015

[391 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
[]
23
--------------------------------------------------------------------------------
                                      NFC	                                      
--------------------------------------------------------------------------------
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'M1C_12-pcw_W2_F_12835', 'M1C_12-pcw_W2_F_12960', 'M1C_12-pcw_W2_F_13060', 'M1C_13-pcw_W2_M_12820', 'M1C_13-pcw_W2_F_12834', 'M1C_13-pcw_W2_M_12888', 'M1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'M1C_21-pcw_W4_M_12886', 'M1C_24-pcw_W4_M_12288', 'M1C_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples  Values                 Conditions
0     M1C-S1C_8-pcw_W1_M_13058     0.0   M1C-S1C_8-pcw_W1_M_13058
1     M1C-S1C_9-pcw_W1_M_12833     0.0   M1C-S1C_9-pcw_W1_M_12833
2        M1C_12-pcw_W2_F_12835     0.0      M1C_12-pcw_W2_F_12835
3        M1C_12-pcw_W2_F_12960     0.0      M1C_12-pcw_W2_F_12960
4        M1C_12-pcw_W2_F_13060     0.0      M1C_12-pcw_W2_F_13060
..                         ...     ...                        ...
340  M1C-S1C_17-pcw_W3_F_12880     0.0  M1C-S1C_17-pcw_W3_F_12880
341  M1C-S1C_19-pcw_W4_F_12885     0.0  M1C-S1C_19-pcw_W4_F_12885
342      M1C_21-pcw_W4_M_12886     0.0      M1C_21-pcw_W4_M_12886
343      M1C_24-pcw_W4_M_12288     0.0      M1C_24-pcw_W4_M_12288
344  M1C_37-pcw_W4_M_263195015     0.0  M1C_37-pcw_W4_M_263195015

[345 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'S1C_12-pcw_W2_F_12835', 'S1C_12-pcw_W2_F_12960', 'S1C_12-pcw_W2_F_13060', 'S1C_13-pcw_W2_M_12820', 'S1C_13-pcw_W2_F_12834', 'S1C_13-pcw_W2_M_12888', 'S1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'S1C_21-pcw_W4_M_12886', 'S1C_24-pcw_W4_M_12288', 'S1C_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0     M1C-S1C_8-pcw_W1_M_13058  0.000000   M1C-S1C_8-pcw_W1_M_13058
1     M1C-S1C_9-pcw_W1_M_12833  0.000000   M1C-S1C_9-pcw_W1_M_12833
2        S1C_12-pcw_W2_F_12835  0.000000      S1C_12-pcw_W2_F_12835
3        S1C_12-pcw_W2_F_12960  0.000000      S1C_12-pcw_W2_F_12960
4        S1C_12-pcw_W2_F_13060  0.000000      S1C_12-pcw_W2_F_13060
..                         ...       ...                        ...
340  M1C-S1C_17-pcw_W3_F_12880  0.000000  M1C-S1C_17-pcw_W3_F_12880
341  M1C-S1C_19-pcw_W4_F_12885  0.000000  M1C-S1C_19-pcw_W4_F_12885
342      S1C_21-pcw_W4_M_12886  0.013582      S1C_21-pcw_W4_M_12886
343      S1C_24-pcw_W4_M_12288  0.000000      S1C_24-pcw_W4_M_12288
344  S1C_37-pcw_W4_M_263195015  0.000000  S1C_37-pcw_W4_M_263195015

[345 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['IPC_12-pcw_W2_F_12835', 'IPC_12-pcw_W2_F_12960', 'IPC_12-pcw_W2_F_13060', 'IPC_13-pcw_W2_M_12820', 'IPC_13-pcw_W2_F_12834', 'IPC_13-pcw_W2_M_12888', 'IPC_16-pcw_W3_M_12287', 'IPC_16-pcw_W3_M_12837', 'IPC_16-pcw_W3_M_12879', 'IPC_17-pcw_W3_F_12880', 'IPC_19-pcw_W4_F_12885', 'IPC_21-pcw_W4_M_12886', 'IPC_24-pcw_W4_M_12288', 'IPC_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        IPC_12-pcw_W2_F_12835  0.000000      IPC_12-pcw_W2_F_12835
1        IPC_12-pcw_W2_F_12960  0.000000      IPC_12-pcw_W2_F_12960
2        IPC_12-pcw_W2_F_13060  0.000000      IPC_12-pcw_W2_F_13060
3        IPC_13-pcw_W2_M_12820  0.000000      IPC_13-pcw_W2_M_12820
4        IPC_13-pcw_W2_F_12834  0.000000      IPC_13-pcw_W2_F_12834
..                         ...       ...                        ...
317      IPC_17-pcw_W3_F_12880  0.000000      IPC_17-pcw_W3_F_12880
318      IPC_19-pcw_W4_F_12885  0.015155      IPC_19-pcw_W4_F_12885
319      IPC_21-pcw_W4_M_12886  0.013964      IPC_21-pcw_W4_M_12886
320      IPC_24-pcw_W4_M_12288  0.000000      IPC_24-pcw_W4_M_12288
321  IPC_37-pcw_W4_M_263195015  0.000000  IPC_37-pcw_W4_M_263195015

[322 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['A1C_12-pcw_W2_F_12835', 'A1C_12-pcw_W2_F_12960', 'A1C_12-pcw_W2_F_13060', 'A1C_13-pcw_W2_M_12820', 'A1C_13-pcw_W2_F_12834', 'A1C_13-pcw_W2_M_12888', 'A1C_16-pcw_W3_M_12287', 'A1C_16-pcw_W3_M_12837', 'A1C_16-pcw_W3_M_12879', 'A1C_17-pcw_W3_F_12880', 'A1C_19-pcw_W4_F_12885', 'A1C_24-pcw_W4_M_12288', 'A1C_25-pcw_W4_F_12948', 'A1C_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        A1C_12-pcw_W2_F_12835  0.000000      A1C_12-pcw_W2_F_12835
1        A1C_12-pcw_W2_F_12960  0.000000      A1C_12-pcw_W2_F_12960
2        A1C_12-pcw_W2_F_13060  0.000000      A1C_12-pcw_W2_F_13060
3        A1C_13-pcw_W2_M_12820  0.000000      A1C_13-pcw_W2_M_12820
4        A1C_13-pcw_W2_F_12834  0.000000      A1C_13-pcw_W2_F_12834
..                         ...       ...                        ...
317      A1C_17-pcw_W3_F_12880  0.000000      A1C_17-pcw_W3_F_12880
318      A1C_19-pcw_W4_F_12885  0.013898      A1C_19-pcw_W4_F_12885
319      A1C_24-pcw_W4_M_12288  0.000000      A1C_24-pcw_W4_M_12288
320      A1C_25-pcw_W4_F_12948  0.000000      A1C_25-pcw_W4_F_12948
321  A1C_37-pcw_W4_M_263195015  0.049277  A1C_37-pcw_W4_M_263195015

[322 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['STC_8-pcw_W1_M_13058', 'STC_12-pcw_W2_F_12835', 'STC_12-pcw_W2_F_12960', 'STC_13-pcw_W2_M_12820', 'STC_13-pcw_W2_F_12834', 'STC_16-pcw_W3_M_12287', 'STC_16-pcw_W3_M_12837', 'STC_16-pcw_W3_M_12879', 'STC_17-pcw_W3_F_12880', 'STC_19-pcw_W4_F_12885', 'STC_21-pcw_W4_M_12886', 'STC_24-pcw_W4_M_12288', 'STC_26-pcw_W4_F_12949', 'STC_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         STC_8-pcw_W1_M_13058  0.000000       STC_8-pcw_W1_M_13058
1        STC_12-pcw_W2_F_12835  0.000000      STC_12-pcw_W2_F_12835
2        STC_12-pcw_W2_F_12960  0.000000      STC_12-pcw_W2_F_12960
3        STC_13-pcw_W2_M_12820  0.000000      STC_13-pcw_W2_M_12820
4        STC_13-pcw_W2_F_12834  0.000000      STC_13-pcw_W2_F_12834
..                         ...       ...                        ...
317      STC_19-pcw_W4_F_12885  0.000000      STC_19-pcw_W4_F_12885
318      STC_21-pcw_W4_M_12886  0.014022      STC_21-pcw_W4_M_12886
319      STC_24-pcw_W4_M_12288  0.019905      STC_24-pcw_W4_M_12288
320      STC_26-pcw_W4_F_12949  0.000000      STC_26-pcw_W4_F_12949
321  STC_37-pcw_W4_M_263195015  0.040643  STC_37-pcw_W4_M_263195015

[322 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
[]
23
--------------------------------------------------------------------------------
                                      ITC	                                      
--------------------------------------------------------------------------------
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['V1C_12-pcw_W2_F_12835', 'V1C_12-pcw_W2_F_12960', 'V1C_12-pcw_W2_F_13060', 'V1C_13-pcw_W2_M_12820', 'V1C_13-pcw_W2_F_12834', 'V1C_13-pcw_W2_M_12888', 'V1C_16-pcw_W3_M_12287', 'V1C_16-pcw_W3_M_12837', 'V1C_16-pcw_W3_M_12879', 'V1C_17-pcw_W3_F_12880', 'V1C_19-pcw_W4_F_12885', 'V1C_21-pcw_W4_M_12886', 'V1C_24-pcw_W4_M_12288', 'V1C_26-pcw_W4_F_12949', 'V1C_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples  Values                 Conditions
0        V1C_12-pcw_W2_F_12835     0.0      V1C_12-pcw_W2_F_12835
1        V1C_12-pcw_W2_F_12960     0.0      V1C_12-pcw_W2_F_12960
2        V1C_12-pcw_W2_F_13060     0.0      V1C_12-pcw_W2_F_13060
3        V1C_13-pcw_W2_M_12820     0.0      V1C_13-pcw_W2_M_12820
4        V1C_13-pcw_W2_F_12834     0.0      V1C_13-pcw_W2_F_12834
..                         ...     ...                        ...
340      V1C_19-pcw_W4_F_12885     0.0      V1C_19-pcw_W4_F_12885
341      V1C_21-pcw_W4_M_12886     0.0      V1C_21-pcw_W4_M_12886
342      V1C_24-pcw_W4_M_12288     0.0      V1C_24-pcw_W4_M_12288
343      V1C_26-pcw_W4_F_12949     0.0      V1C_26-pcw_W4_F_12949
344  V1C_37-pcw_W4_M_263195015     0.0  V1C_37-pcw_W4_M_263195015

[345 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['HIP_8-pcw_W1_M_13058', 'HIP_9-pcw_W1_M_12833', 'HIP_12-pcw_W2_F_12835', 'HIP_12-pcw_W2_F_12960', 'HIP_12-pcw_W2_F_13060', 'HIP_13-pcw_W2_M_12820', 'HIP_13-pcw_W2_F_12834', 'HIP_13-pcw_W2_M_12888', 'HIP_16-pcw_W3_M_12837', 'HIP_16-pcw_W3_M_12879', 'HIP_17-pcw_W3_F_12880', 'HIP_19-pcw_W4_F_12885', 'HIP_21-pcw_W4_M_12886', 'HIP_24-pcw_W4_M_12288', 'HIP_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         HIP_8-pcw_W1_M_13058  0.000000       HIP_8-pcw_W1_M_13058
1         HIP_9-pcw_W1_M_12833  0.000000       HIP_9-pcw_W1_M_12833
2        HIP_12-pcw_W2_F_12835  0.000000      HIP_12-pcw_W2_F_12835
3        HIP_12-pcw_W2_F_12960  0.000000      HIP_12-pcw_W2_F_12960
4        HIP_12-pcw_W2_F_13060  0.000000      HIP_12-pcw_W2_F_13060
..                         ...       ...                        ...
340      HIP_17-pcw_W3_F_12880  0.000000      HIP_17-pcw_W3_F_12880
341      HIP_19-pcw_W4_F_12885  0.000000      HIP_19-pcw_W4_F_12885
342      HIP_21-pcw_W4_M_12886  0.000000      HIP_21-pcw_W4_M_12886
343      HIP_24-pcw_W4_M_12288  0.000000      HIP_24-pcw_W4_M_12288
344  HIP_37-pcw_W4_M_263195015  0.020955  HIP_37-pcw_W4_M_263195015

[345 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['AMY_8-pcw_W1_M_13058', 'AMY_9-pcw_W1_M_12833', 'AMY_12-pcw_W2_F_12835', 'AMY_12-pcw_W2_F_12960', 'AMY_12-pcw_W2_F_13060', 'AMY_13-pcw_W2_M_12820', 'AMY_13-pcw_W2_F_12834', 'AMY_13-pcw_W2_M_12888', 'AMY_16-pcw_W3_M_12837', 'AMY_16-pcw_W3_M_12879', 'AMY_17-pcw_W3_F_12880', 'AMY_21-pcw_W4_M_12886', 'AMY_24-pcw_W4_M_12288', 'AMY_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         AMY_8-pcw_W1_M_13058  0.000000       AMY_8-pcw_W1_M_13058
1         AMY_9-pcw_W1_M_12833  0.000000       AMY_9-pcw_W1_M_12833
2        AMY_12-pcw_W2_F_12835  0.000000      AMY_12-pcw_W2_F_12835
3        AMY_12-pcw_W2_F_12960  0.000000      AMY_12-pcw_W2_F_12960
4        AMY_12-pcw_W2_F_13060  0.000000      AMY_12-pcw_W2_F_13060
..                         ...       ...                        ...
317      AMY_16-pcw_W3_M_12879  0.000000      AMY_16-pcw_W3_M_12879
318      AMY_17-pcw_W3_F_12880  0.026827      AMY_17-pcw_W3_F_12880
319      AMY_21-pcw_W4_M_12886  0.000000      AMY_21-pcw_W4_M_12886
320      AMY_24-pcw_W4_M_12288  0.000000      AMY_24-pcw_W4_M_12288
321  AMY_37-pcw_W4_M_263195015  0.000000  AMY_37-pcw_W4_M_263195015

[322 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['STR_12-pcw_W2_F_12835', 'STR_12-pcw_W2_F_12960', 'STR_12-pcw_W2_F_13060', 'STR_13-pcw_W2_M_12820', 'STR_13-pcw_W2_F_12834', 'STR_13-pcw_W2_M_12888', 'STR_16-pcw_W3_M_12287', 'STR_16-pcw_W3_M_12837', 'STR_16-pcw_W3_M_12879', 'STR_17-pcw_W3_F_12880', 'STR_19-pcw_W4_F_12885', 'STR_21-pcw_W4_M_12886', 'STR_24-pcw_W4_M_12288', 'STR_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        STR_12-pcw_W2_F_12835  0.000000      STR_12-pcw_W2_F_12835
1        STR_12-pcw_W2_F_12960  0.000000      STR_12-pcw_W2_F_12960
2        STR_12-pcw_W2_F_13060  0.000000      STR_12-pcw_W2_F_13060
3        STR_13-pcw_W2_M_12820  0.000000      STR_13-pcw_W2_M_12820
4        STR_13-pcw_W2_F_12834  0.000000      STR_13-pcw_W2_F_12834
..                         ...       ...                        ...
317      STR_17-pcw_W3_F_12880  0.000000      STR_17-pcw_W3_F_12880
318      STR_19-pcw_W4_F_12885  0.000000      STR_19-pcw_W4_F_12885
319      STR_21-pcw_W4_M_12886  0.000000      STR_21-pcw_W4_M_12886
320      STR_24-pcw_W4_M_12288  0.018228      STR_24-pcw_W4_M_12288
321  STR_37-pcw_W4_M_263195015  0.000000  STR_37-pcw_W4_M_263195015

[322 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['MD_13-pcw_W2_F_12834', 'MD_16-pcw_W3_M_12287', 'MD_16-pcw_W3_M_12837', 'MD_16-pcw_W3_M_12879', 'MD_17-pcw_W3_F_12880', 'MD_19-pcw_W4_F_12885', 'MD_24-pcw_W4_M_12288', 'MD_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                      Samples  Values                Conditions
0        MD_13-pcw_W2_F_12834     0.0      MD_13-pcw_W2_F_12834
1        MD_16-pcw_W3_M_12287     0.0      MD_16-pcw_W3_M_12287
2        MD_16-pcw_W3_M_12837     0.0      MD_16-pcw_W3_M_12837
3        MD_16-pcw_W3_M_12879     0.0      MD_16-pcw_W3_M_12879
4        MD_17-pcw_W3_F_12880     0.0      MD_17-pcw_W3_F_12880
..                        ...     ...                       ...
179      MD_16-pcw_W3_M_12879     0.0      MD_16-pcw_W3_M_12879
180      MD_17-pcw_W3_F_12880     0.0      MD_17-pcw_W3_F_12880
181      MD_19-pcw_W4_F_12885     0.0      MD_19-pcw_W4_F_12885
182      MD_24-pcw_W4_M_12288     0.0      MD_24-pcw_W4_M_12288
183  MD_37-pcw_W4_M_263195015     0.0  MD_37-pcw_W4_M_263195015

[184 rows x 3 columns]
[77, 1394, 1437, 1846, 3274, 4187, 4562, 6120, 6121, 6122, 6538, 7372, 8679, 8814, 9122, 10422, 10464, 11375, 13951, 14786, 15659, 18133, 47325]
['CBC_12-pcw_W2_F_13060', 'CBC_16-pcw_W3_M_12837', 'CBC_17-pcw_W3_F_12880', 'CBC_21-pcw_W4_F_12365', 'CBC_21-pcw_W4_M_12886', 'CBC_24-pcw_W4_M_12288', 'CBC_35-pcw_W4_F_12295', 'CBC_37-pcw_W4_M_263195015']
23
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        CBC_12-pcw_W2_F_13060  0.000000      CBC_12-pcw_W2_F_13060
1        CBC_16-pcw_W3_M_12837  0.000000      CBC_16-pcw_W3_M_12837
2        CBC_17-pcw_W3_F_12880  0.000000      CBC_17-pcw_W3_F_12880
3        CBC_21-pcw_W4_F_12365  0.000000      CBC_21-pcw_W4_F_12365
4        CBC_21-pcw_W4_M_12886  0.000000      CBC_21-pcw_W4_M_12886
..                         ...       ...                        ...
179      CBC_21-pcw_W4_F_12365  0.008741      CBC_21-pcw_W4_F_12365
180      CBC_21-pcw_W4_M_12886  0.000000      CBC_21-pcw_W4_M_12886
181      CBC_24-pcw_W4_M_12288  0.000000      CBC_24-pcw_W4_M_12288
182      CBC_35-pcw_W4_F_12295  0.000000      CBC_35-pcw_W4_F_12295
183  CBC_37-pcw_W4_M_263195015  0.000000  CBC_37-pcw_W4_M_263195015

[184 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['MFC_8-pcw_W1_M_13058', 'MFC_9-pcw_W1_M_12833', 'MFC_12-pcw_W2_F_12835', 'MFC_12-pcw_W2_F_13060', 'MFC_13-pcw_W2_M_12820', 'MFC_13-pcw_W2_F_12834', 'MFC_13-pcw_W2_M_12888', 'MFC_16-pcw_W3_M_12287', 'MFC_16-pcw_W3_M_12837', 'MFC_16-pcw_W3_M_12879', 'MFC_17-pcw_W3_F_12880', 'MFC_19-pcw_W4_F_12885', 'MFC_21-pcw_W4_M_12886', 'MFC_24-pcw_W4_M_12288', 'MFC_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          MFC_8-pcw_W1_M_13058  4.078432       MFC_8-pcw_W1_M_13058
1          MFC_9-pcw_W1_M_12833  4.693058       MFC_9-pcw_W1_M_12833
2         MFC_12-pcw_W2_F_12835  2.313433      MFC_12-pcw_W2_F_12835
3         MFC_12-pcw_W2_F_13060  2.422125      MFC_12-pcw_W2_F_13060
4         MFC_13-pcw_W2_M_12820  2.559028      MFC_13-pcw_W2_M_12820
...                         ...       ...                        ...
1840      MFC_17-pcw_W3_F_12880  1.083316      MFC_17-pcw_W3_F_12880
1841      MFC_19-pcw_W4_F_12885  1.497498      MFC_19-pcw_W4_F_12885
1842      MFC_21-pcw_W4_M_12886  1.621409      MFC_21-pcw_W4_M_12886
1843      MFC_24-pcw_W4_M_12288  1.043057      MFC_24-pcw_W4_M_12288
1844  MFC_37-pcw_W4_M_263195015  2.952003  MFC_37-pcw_W4_M_263195015

[1845 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['OFC_8-pcw_W1_M_13058', 'OFC_9-pcw_W1_M_12833', 'OFC_12-pcw_W2_F_12835', 'OFC_12-pcw_W2_F_12960', 'OFC_12-pcw_W2_F_13060', 'OFC_13-pcw_W2_M_12820', 'OFC_13-pcw_W2_F_12834', 'OFC_13-pcw_W2_M_12888', 'OFC_16-pcw_W3_M_12287', 'OFC_16-pcw_W3_M_12837', 'OFC_17-pcw_W3_F_12880', 'OFC_21-pcw_W4_M_12886', 'OFC_24-pcw_W4_M_12288', 'OFC_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          OFC_8-pcw_W1_M_13058  2.800110       OFC_8-pcw_W1_M_13058
1          OFC_9-pcw_W1_M_12833  4.965999       OFC_9-pcw_W1_M_12833
2         OFC_12-pcw_W2_F_12835  2.545520      OFC_12-pcw_W2_F_12835
3         OFC_12-pcw_W2_F_12960  1.906490      OFC_12-pcw_W2_F_12960
4         OFC_12-pcw_W2_F_13060  2.230151      OFC_12-pcw_W2_F_13060
...                         ...       ...                        ...
1717      OFC_16-pcw_W3_M_12837  0.824800      OFC_16-pcw_W3_M_12837
1718      OFC_17-pcw_W3_F_12880  1.207623      OFC_17-pcw_W3_F_12880
1719      OFC_21-pcw_W4_M_12886  1.513665      OFC_21-pcw_W4_M_12886
1720      OFC_24-pcw_W4_M_12288  0.731201      OFC_24-pcw_W4_M_12288
1721  OFC_37-pcw_W4_M_263195015  2.894436  OFC_37-pcw_W4_M_263195015

[1722 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['DFC_8-pcw_W1_M_13058', 'DFC_9-pcw_W1_M_12833', 'DFC_12-pcw_W2_F_12835', 'DFC_12-pcw_W2_F_12960', 'DFC_12-pcw_W2_F_13060', 'DFC_13-pcw_W2_M_12820', 'DFC_13-pcw_W2_F_12834', 'DFC_13-pcw_W2_M_12888', 'DFC_16-pcw_W3_M_12287', 'DFC_16-pcw_W3_M_12837', 'DFC_16-pcw_W3_M_12879', 'DFC_17-pcw_W3_F_12880', 'DFC_19-pcw_W4_F_12885', 'DFC_21-pcw_W4_M_12886', 'DFC_24-pcw_W4_M_12288', 'DFC_26-pcw_W4_F_12949', 'DFC_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          DFC_8-pcw_W1_M_13058  3.495410       DFC_8-pcw_W1_M_13058
1          DFC_9-pcw_W1_M_12833  4.694286       DFC_9-pcw_W1_M_12833
2         DFC_12-pcw_W2_F_12835  2.588386      DFC_12-pcw_W2_F_12835
3         DFC_12-pcw_W2_F_12960  1.951976      DFC_12-pcw_W2_F_12960
4         DFC_12-pcw_W2_F_13060  2.425832      DFC_12-pcw_W2_F_13060
...                         ...       ...                        ...
2086      DFC_19-pcw_W4_F_12885  1.272555      DFC_19-pcw_W4_F_12885
2087      DFC_21-pcw_W4_M_12886  1.855120      DFC_21-pcw_W4_M_12886
2088      DFC_24-pcw_W4_M_12288  1.119783      DFC_24-pcw_W4_M_12288
2089      DFC_26-pcw_W4_F_12949  1.399452      DFC_26-pcw_W4_F_12949
2090  DFC_37-pcw_W4_M_263195015  3.166494  DFC_37-pcw_W4_M_263195015

[2091 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
[]
123
--------------------------------------------------------------------------------
                                      NFC	                                      
--------------------------------------------------------------------------------
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'M1C_12-pcw_W2_F_12835', 'M1C_12-pcw_W2_F_12960', 'M1C_12-pcw_W2_F_13060', 'M1C_13-pcw_W2_M_12820', 'M1C_13-pcw_W2_F_12834', 'M1C_13-pcw_W2_M_12888', 'M1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'M1C_21-pcw_W4_M_12886', 'M1C_24-pcw_W4_M_12288', 'M1C_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0      M1C-S1C_8-pcw_W1_M_13058  4.628221   M1C-S1C_8-pcw_W1_M_13058
1      M1C-S1C_9-pcw_W1_M_12833  4.857867   M1C-S1C_9-pcw_W1_M_12833
2         M1C_12-pcw_W2_F_12835  2.453591      M1C_12-pcw_W2_F_12835
3         M1C_12-pcw_W2_F_12960  2.248356      M1C_12-pcw_W2_F_12960
4         M1C_12-pcw_W2_F_13060  1.754709      M1C_12-pcw_W2_F_13060
...                         ...       ...                        ...
1840  M1C-S1C_17-pcw_W3_F_12880  1.139700  M1C-S1C_17-pcw_W3_F_12880
1841  M1C-S1C_19-pcw_W4_F_12885  1.297919  M1C-S1C_19-pcw_W4_F_12885
1842      M1C_21-pcw_W4_M_12886  1.241486      M1C_21-pcw_W4_M_12886
1843      M1C_24-pcw_W4_M_12288  1.648517      M1C_24-pcw_W4_M_12288
1844  M1C_37-pcw_W4_M_263195015  3.531418  M1C_37-pcw_W4_M_263195015

[1845 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'S1C_12-pcw_W2_F_12835', 'S1C_12-pcw_W2_F_12960', 'S1C_12-pcw_W2_F_13060', 'S1C_13-pcw_W2_M_12820', 'S1C_13-pcw_W2_F_12834', 'S1C_13-pcw_W2_M_12888', 'S1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'S1C_21-pcw_W4_M_12886', 'S1C_24-pcw_W4_M_12288', 'S1C_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0      M1C-S1C_8-pcw_W1_M_13058  4.628221   M1C-S1C_8-pcw_W1_M_13058
1      M1C-S1C_9-pcw_W1_M_12833  4.857867   M1C-S1C_9-pcw_W1_M_12833
2         S1C_12-pcw_W2_F_12835  2.260516      S1C_12-pcw_W2_F_12835
3         S1C_12-pcw_W2_F_12960  1.912743      S1C_12-pcw_W2_F_12960
4         S1C_12-pcw_W2_F_13060  1.827513      S1C_12-pcw_W2_F_13060
...                         ...       ...                        ...
1840  M1C-S1C_17-pcw_W3_F_12880  1.139700  M1C-S1C_17-pcw_W3_F_12880
1841  M1C-S1C_19-pcw_W4_F_12885  1.297919  M1C-S1C_19-pcw_W4_F_12885
1842      S1C_21-pcw_W4_M_12886  1.408406      S1C_21-pcw_W4_M_12886
1843      S1C_24-pcw_W4_M_12288  1.844811      S1C_24-pcw_W4_M_12288
1844  S1C_37-pcw_W4_M_263195015  3.448874  S1C_37-pcw_W4_M_263195015

[1845 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['IPC_12-pcw_W2_F_12835', 'IPC_12-pcw_W2_F_12960', 'IPC_12-pcw_W2_F_13060', 'IPC_13-pcw_W2_M_12820', 'IPC_13-pcw_W2_F_12834', 'IPC_13-pcw_W2_M_12888', 'IPC_16-pcw_W3_M_12287', 'IPC_16-pcw_W3_M_12837', 'IPC_16-pcw_W3_M_12879', 'IPC_17-pcw_W3_F_12880', 'IPC_19-pcw_W4_F_12885', 'IPC_21-pcw_W4_M_12886', 'IPC_24-pcw_W4_M_12288', 'IPC_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0         IPC_12-pcw_W2_F_12835  1.914591      IPC_12-pcw_W2_F_12835
1         IPC_12-pcw_W2_F_12960  1.878592      IPC_12-pcw_W2_F_12960
2         IPC_12-pcw_W2_F_13060  1.390381      IPC_12-pcw_W2_F_13060
3         IPC_13-pcw_W2_M_12820  1.763910      IPC_13-pcw_W2_M_12820
4         IPC_13-pcw_W2_F_12834  1.679115      IPC_13-pcw_W2_F_12834
...                         ...       ...                        ...
1717      IPC_17-pcw_W3_F_12880  0.652817      IPC_17-pcw_W3_F_12880
1718      IPC_19-pcw_W4_F_12885  1.234601      IPC_19-pcw_W4_F_12885
1719      IPC_21-pcw_W4_M_12886  1.410621      IPC_21-pcw_W4_M_12886
1720      IPC_24-pcw_W4_M_12288  1.416537      IPC_24-pcw_W4_M_12288
1721  IPC_37-pcw_W4_M_263195015  3.279734  IPC_37-pcw_W4_M_263195015

[1722 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['A1C_12-pcw_W2_F_12835', 'A1C_12-pcw_W2_F_12960', 'A1C_12-pcw_W2_F_13060', 'A1C_13-pcw_W2_M_12820', 'A1C_13-pcw_W2_F_12834', 'A1C_13-pcw_W2_M_12888', 'A1C_16-pcw_W3_M_12287', 'A1C_16-pcw_W3_M_12837', 'A1C_16-pcw_W3_M_12879', 'A1C_17-pcw_W3_F_12880', 'A1C_19-pcw_W4_F_12885', 'A1C_24-pcw_W4_M_12288', 'A1C_25-pcw_W4_F_12948', 'A1C_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0         A1C_12-pcw_W2_F_12835  2.263093      A1C_12-pcw_W2_F_12835
1         A1C_12-pcw_W2_F_12960  2.119267      A1C_12-pcw_W2_F_12960
2         A1C_12-pcw_W2_F_13060  1.897889      A1C_12-pcw_W2_F_13060
3         A1C_13-pcw_W2_M_12820  3.255854      A1C_13-pcw_W2_M_12820
4         A1C_13-pcw_W2_F_12834  2.392181      A1C_13-pcw_W2_F_12834
...                         ...       ...                        ...
1717      A1C_17-pcw_W3_F_12880  1.226899      A1C_17-pcw_W3_F_12880
1718      A1C_19-pcw_W4_F_12885  1.236252      A1C_19-pcw_W4_F_12885
1719      A1C_24-pcw_W4_M_12288  1.257268      A1C_24-pcw_W4_M_12288
1720      A1C_25-pcw_W4_F_12948  1.249716      A1C_25-pcw_W4_F_12948
1721  A1C_37-pcw_W4_M_263195015  3.291934  A1C_37-pcw_W4_M_263195015

[1722 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['STC_8-pcw_W1_M_13058', 'STC_12-pcw_W2_F_12835', 'STC_12-pcw_W2_F_12960', 'STC_13-pcw_W2_M_12820', 'STC_13-pcw_W2_F_12834', 'STC_16-pcw_W3_M_12287', 'STC_16-pcw_W3_M_12837', 'STC_16-pcw_W3_M_12879', 'STC_17-pcw_W3_F_12880', 'STC_19-pcw_W4_F_12885', 'STC_21-pcw_W4_M_12886', 'STC_24-pcw_W4_M_12288', 'STC_26-pcw_W4_F_12949', 'STC_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          STC_8-pcw_W1_M_13058  3.924430       STC_8-pcw_W1_M_13058
1         STC_12-pcw_W2_F_12835  2.369710      STC_12-pcw_W2_F_12835
2         STC_12-pcw_W2_F_12960  2.358382      STC_12-pcw_W2_F_12960
3         STC_13-pcw_W2_M_12820  2.552731      STC_13-pcw_W2_M_12820
4         STC_13-pcw_W2_F_12834  1.952346      STC_13-pcw_W2_F_12834
...                         ...       ...                        ...
1717      STC_19-pcw_W4_F_12885  1.598933      STC_19-pcw_W4_F_12885
1718      STC_21-pcw_W4_M_12886  1.658296      STC_21-pcw_W4_M_12886
1719      STC_24-pcw_W4_M_12288  1.914944      STC_24-pcw_W4_M_12288
1720      STC_26-pcw_W4_F_12949  0.891021      STC_26-pcw_W4_F_12949
1721  STC_37-pcw_W4_M_263195015  3.726876  STC_37-pcw_W4_M_263195015

[1722 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
[]
123
--------------------------------------------------------------------------------
                                      ITC	                                      
--------------------------------------------------------------------------------
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['V1C_12-pcw_W2_F_12835', 'V1C_12-pcw_W2_F_12960', 'V1C_12-pcw_W2_F_13060', 'V1C_13-pcw_W2_M_12820', 'V1C_13-pcw_W2_F_12834', 'V1C_13-pcw_W2_M_12888', 'V1C_16-pcw_W3_M_12287', 'V1C_16-pcw_W3_M_12837', 'V1C_16-pcw_W3_M_12879', 'V1C_17-pcw_W3_F_12880', 'V1C_19-pcw_W4_F_12885', 'V1C_21-pcw_W4_M_12886', 'V1C_24-pcw_W4_M_12288', 'V1C_26-pcw_W4_F_12949', 'V1C_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0         V1C_12-pcw_W2_F_12835  1.361194      V1C_12-pcw_W2_F_12835
1         V1C_12-pcw_W2_F_12960  1.816741      V1C_12-pcw_W2_F_12960
2         V1C_12-pcw_W2_F_13060  1.875868      V1C_12-pcw_W2_F_13060
3         V1C_13-pcw_W2_M_12820  1.736974      V1C_13-pcw_W2_M_12820
4         V1C_13-pcw_W2_F_12834  2.211991      V1C_13-pcw_W2_F_12834
...                         ...       ...                        ...
1840      V1C_19-pcw_W4_F_12885  1.256171      V1C_19-pcw_W4_F_12885
1841      V1C_21-pcw_W4_M_12886  1.412561      V1C_21-pcw_W4_M_12886
1842      V1C_24-pcw_W4_M_12288  1.169564      V1C_24-pcw_W4_M_12288
1843      V1C_26-pcw_W4_F_12949  1.360465      V1C_26-pcw_W4_F_12949
1844  V1C_37-pcw_W4_M_263195015  3.616885  V1C_37-pcw_W4_M_263195015

[1845 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['HIP_8-pcw_W1_M_13058', 'HIP_9-pcw_W1_M_12833', 'HIP_12-pcw_W2_F_12835', 'HIP_12-pcw_W2_F_12960', 'HIP_12-pcw_W2_F_13060', 'HIP_13-pcw_W2_M_12820', 'HIP_13-pcw_W2_F_12834', 'HIP_13-pcw_W2_M_12888', 'HIP_16-pcw_W3_M_12837', 'HIP_16-pcw_W3_M_12879', 'HIP_17-pcw_W3_F_12880', 'HIP_19-pcw_W4_F_12885', 'HIP_21-pcw_W4_M_12886', 'HIP_24-pcw_W4_M_12288', 'HIP_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          HIP_8-pcw_W1_M_13058  4.057145       HIP_8-pcw_W1_M_13058
1          HIP_9-pcw_W1_M_12833  4.197710       HIP_9-pcw_W1_M_12833
2         HIP_12-pcw_W2_F_12835  3.562507      HIP_12-pcw_W2_F_12835
3         HIP_12-pcw_W2_F_12960  3.988279      HIP_12-pcw_W2_F_12960
4         HIP_12-pcw_W2_F_13060  3.746310      HIP_12-pcw_W2_F_13060
...                         ...       ...                        ...
1840      HIP_17-pcw_W3_F_12880  2.862925      HIP_17-pcw_W3_F_12880
1841      HIP_19-pcw_W4_F_12885  2.030300      HIP_19-pcw_W4_F_12885
1842      HIP_21-pcw_W4_M_12886  1.759323      HIP_21-pcw_W4_M_12886
1843      HIP_24-pcw_W4_M_12288  1.747954      HIP_24-pcw_W4_M_12288
1844  HIP_37-pcw_W4_M_263195015  3.205307  HIP_37-pcw_W4_M_263195015

[1845 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['AMY_8-pcw_W1_M_13058', 'AMY_9-pcw_W1_M_12833', 'AMY_12-pcw_W2_F_12835', 'AMY_12-pcw_W2_F_12960', 'AMY_12-pcw_W2_F_13060', 'AMY_13-pcw_W2_M_12820', 'AMY_13-pcw_W2_F_12834', 'AMY_13-pcw_W2_M_12888', 'AMY_16-pcw_W3_M_12837', 'AMY_16-pcw_W3_M_12879', 'AMY_17-pcw_W3_F_12880', 'AMY_21-pcw_W4_M_12886', 'AMY_24-pcw_W4_M_12288', 'AMY_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          AMY_8-pcw_W1_M_13058  5.814393       AMY_8-pcw_W1_M_13058
1          AMY_9-pcw_W1_M_12833  4.984729       AMY_9-pcw_W1_M_12833
2         AMY_12-pcw_W2_F_12835  5.478054      AMY_12-pcw_W2_F_12835
3         AMY_12-pcw_W2_F_12960  4.565334      AMY_12-pcw_W2_F_12960
4         AMY_12-pcw_W2_F_13060  4.297684      AMY_12-pcw_W2_F_13060
...                         ...       ...                        ...
1717      AMY_16-pcw_W3_M_12879  1.201409      AMY_16-pcw_W3_M_12879
1718      AMY_17-pcw_W3_F_12880  1.734889      AMY_17-pcw_W3_F_12880
1719      AMY_21-pcw_W4_M_12886  1.670234      AMY_21-pcw_W4_M_12886
1720      AMY_24-pcw_W4_M_12288  1.367679      AMY_24-pcw_W4_M_12288
1721  AMY_37-pcw_W4_M_263195015  2.665856  AMY_37-pcw_W4_M_263195015

[1722 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['STR_12-pcw_W2_F_12835', 'STR_12-pcw_W2_F_12960', 'STR_12-pcw_W2_F_13060', 'STR_13-pcw_W2_M_12820', 'STR_13-pcw_W2_F_12834', 'STR_13-pcw_W2_M_12888', 'STR_16-pcw_W3_M_12287', 'STR_16-pcw_W3_M_12837', 'STR_16-pcw_W3_M_12879', 'STR_17-pcw_W3_F_12880', 'STR_19-pcw_W4_F_12885', 'STR_21-pcw_W4_M_12886', 'STR_24-pcw_W4_M_12288', 'STR_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0         STR_12-pcw_W2_F_12835  2.182896      STR_12-pcw_W2_F_12835
1         STR_12-pcw_W2_F_12960  4.697683      STR_12-pcw_W2_F_12960
2         STR_12-pcw_W2_F_13060  5.268308      STR_12-pcw_W2_F_13060
3         STR_13-pcw_W2_M_12820  5.101509      STR_13-pcw_W2_M_12820
4         STR_13-pcw_W2_F_12834  5.485142      STR_13-pcw_W2_F_12834
...                         ...       ...                        ...
1717      STR_17-pcw_W3_F_12880  2.788731      STR_17-pcw_W3_F_12880
1718      STR_19-pcw_W4_F_12885  2.092049      STR_19-pcw_W4_F_12885
1719      STR_21-pcw_W4_M_12886  2.791722      STR_21-pcw_W4_M_12886
1720      STR_24-pcw_W4_M_12288  2.170310      STR_24-pcw_W4_M_12288
1721  STR_37-pcw_W4_M_263195015  2.646824  STR_37-pcw_W4_M_263195015

[1722 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['MD_13-pcw_W2_F_12834', 'MD_16-pcw_W3_M_12287', 'MD_16-pcw_W3_M_12837', 'MD_16-pcw_W3_M_12879', 'MD_17-pcw_W3_F_12880', 'MD_19-pcw_W4_F_12885', 'MD_24-pcw_W4_M_12288', 'MD_37-pcw_W4_M_263195015']
123
No handles with labels found to put in legend.
                      Samples    Values                Conditions
0        MD_13-pcw_W2_F_12834  0.615520      MD_13-pcw_W2_F_12834
1        MD_16-pcw_W3_M_12287  2.207468      MD_16-pcw_W3_M_12287
2        MD_16-pcw_W3_M_12837  0.327447      MD_16-pcw_W3_M_12837
3        MD_16-pcw_W3_M_12879  0.879116      MD_16-pcw_W3_M_12879
4        MD_17-pcw_W3_F_12880  1.526801      MD_17-pcw_W3_F_12880
..                        ...       ...                       ...
979      MD_16-pcw_W3_M_12879  0.718105      MD_16-pcw_W3_M_12879
980      MD_17-pcw_W3_F_12880  2.196308      MD_17-pcw_W3_F_12880
981      MD_19-pcw_W4_F_12885  1.660407      MD_19-pcw_W4_F_12885
982      MD_24-pcw_W4_M_12288  1.491351      MD_24-pcw_W4_M_12288
983  MD_37-pcw_W4_M_263195015  2.584815  MD_37-pcw_W4_M_263195015

[984 rows x 3 columns]
[64, 114, 208, 370, 604, 613, 694, 713, 770, 889, 1040, 1112, 1269, 1485, 1530, 1917, 2008, 2011, 2195, 2436, 2452, 2467, 2491, 2542, 3379, 3629, 3635, 3744, 3785, 3895, 3997, 4090, 4210, 4314, 4471, 4479, 4532, 4568, 4573, 4650, 4744, 4960, 5013, 5132, 5343, 5945, 6096, 6551, 6693, 6895, 6985, 6999, 7024, 7244, 7676, 7678, 7886, 7892, 8043, 8168, 8287, 8360, 8379, 8637, 8758, 8781, 8860, 8911, 8927, 9223, 9566, 9590, 10313, 10463, 10678, 10832, 10937, 11008, 11153, 11163, 11220, 11265, 11372, 11545, 11700, 11764, 11985, 12075, 12337, 12424, 12472, 12684, 13305, 13326, 13452, 13635, 13680, 13995, 14129, 14216, 14464, 14519, 14694, 14809, 14912, 15144, 15299, 15500, 15880, 15938, 16020, 16029, 16342, 16629, 16952, 16970, 17590, 17997, 18076, 18240, 18395, 23573, 42509]
['CBC_12-pcw_W2_F_13060', 'CBC_16-pcw_W3_M_12837', 'CBC_17-pcw_W3_F_12880', 'CBC_21-pcw_W4_F_12365', 'CBC_21-pcw_W4_M_12886', 'CBC_24-pcw_W4_M_12288', 'CBC_35-pcw_W4_F_12295', 'CBC_37-pcw_W4_M_263195015']
123
                       Samples    Values                 Conditions
0        CBC_12-pcw_W2_F_13060  0.000000      CBC_12-pcw_W2_F_13060
1        CBC_16-pcw_W3_M_12837  0.097164      CBC_16-pcw_W3_M_12837
2        CBC_17-pcw_W3_F_12880  0.471328      CBC_17-pcw_W3_F_12880
3        CBC_21-pcw_W4_F_12365  0.144455      CBC_21-pcw_W4_F_12365
4        CBC_21-pcw_W4_M_12886  0.122604      CBC_21-pcw_W4_M_12886
..                         ...       ...                        ...
979      CBC_21-pcw_W4_F_12365  1.107663      CBC_21-pcw_W4_F_12365
980      CBC_21-pcw_W4_M_12886  1.367089      CBC_21-pcw_W4_M_12886
981      CBC_24-pcw_W4_M_12288  1.469842      CBC_24-pcw_W4_M_12288
982      CBC_35-pcw_W4_F_12295  0.481463      CBC_35-pcw_W4_F_12295
983  CBC_37-pcw_W4_M_263195015  2.637411  CBC_37-pcw_W4_M_263195015

[984 rows x 3 columns]
No handles with labels found to put in legend.
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['MFC_8-pcw_W1_M_13058', 'MFC_9-pcw_W1_M_12833', 'MFC_12-pcw_W2_F_12835', 'MFC_12-pcw_W2_F_13060', 'MFC_13-pcw_W2_M_12820', 'MFC_13-pcw_W2_F_12834', 'MFC_13-pcw_W2_M_12888', 'MFC_16-pcw_W3_M_12287', 'MFC_16-pcw_W3_M_12837', 'MFC_16-pcw_W3_M_12879', 'MFC_17-pcw_W3_F_12880', 'MFC_19-pcw_W4_F_12885', 'MFC_21-pcw_W4_M_12886', 'MFC_24-pcw_W4_M_12288', 'MFC_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         MFC_8-pcw_W1_M_13058  0.015036       MFC_8-pcw_W1_M_13058
1         MFC_9-pcw_W1_M_12833  0.167486       MFC_9-pcw_W1_M_12833
2        MFC_12-pcw_W2_F_12835  0.000000      MFC_12-pcw_W2_F_12835
3        MFC_12-pcw_W2_F_13060  0.000000      MFC_12-pcw_W2_F_13060
4        MFC_13-pcw_W2_M_12820  0.000000      MFC_13-pcw_W2_M_12820
..                         ...       ...                        ...
640      MFC_17-pcw_W3_F_12880  0.000000      MFC_17-pcw_W3_F_12880
641      MFC_19-pcw_W4_F_12885  0.024921      MFC_19-pcw_W4_F_12885
642      MFC_21-pcw_W4_M_12886  0.000000      MFC_21-pcw_W4_M_12886
643      MFC_24-pcw_W4_M_12288  0.000000      MFC_24-pcw_W4_M_12288
644  MFC_37-pcw_W4_M_263195015  0.000000  MFC_37-pcw_W4_M_263195015

[645 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['OFC_8-pcw_W1_M_13058', 'OFC_9-pcw_W1_M_12833', 'OFC_12-pcw_W2_F_12835', 'OFC_12-pcw_W2_F_12960', 'OFC_12-pcw_W2_F_13060', 'OFC_13-pcw_W2_M_12820', 'OFC_13-pcw_W2_F_12834', 'OFC_13-pcw_W2_M_12888', 'OFC_16-pcw_W3_M_12287', 'OFC_16-pcw_W3_M_12837', 'OFC_17-pcw_W3_F_12880', 'OFC_21-pcw_W4_M_12886', 'OFC_24-pcw_W4_M_12288', 'OFC_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         OFC_8-pcw_W1_M_13058  0.368730       OFC_8-pcw_W1_M_13058
1         OFC_9-pcw_W1_M_12833  0.329267       OFC_9-pcw_W1_M_12833
2        OFC_12-pcw_W2_F_12835  0.000000      OFC_12-pcw_W2_F_12835
3        OFC_12-pcw_W2_F_12960  0.000000      OFC_12-pcw_W2_F_12960
4        OFC_12-pcw_W2_F_13060  0.011428      OFC_12-pcw_W2_F_13060
..                         ...       ...                        ...
597      OFC_16-pcw_W3_M_12837  0.000000      OFC_16-pcw_W3_M_12837
598      OFC_17-pcw_W3_F_12880  0.000000      OFC_17-pcw_W3_F_12880
599      OFC_21-pcw_W4_M_12886  0.000000      OFC_21-pcw_W4_M_12886
600      OFC_24-pcw_W4_M_12288  0.000000      OFC_24-pcw_W4_M_12288
601  OFC_37-pcw_W4_M_263195015  0.000000  OFC_37-pcw_W4_M_263195015

[602 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['DFC_8-pcw_W1_M_13058', 'DFC_9-pcw_W1_M_12833', 'DFC_12-pcw_W2_F_12835', 'DFC_12-pcw_W2_F_12960', 'DFC_12-pcw_W2_F_13060', 'DFC_13-pcw_W2_M_12820', 'DFC_13-pcw_W2_F_12834', 'DFC_13-pcw_W2_M_12888', 'DFC_16-pcw_W3_M_12287', 'DFC_16-pcw_W3_M_12837', 'DFC_16-pcw_W3_M_12879', 'DFC_17-pcw_W3_F_12880', 'DFC_19-pcw_W4_F_12885', 'DFC_21-pcw_W4_M_12886', 'DFC_24-pcw_W4_M_12288', 'DFC_26-pcw_W4_F_12949', 'DFC_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         DFC_8-pcw_W1_M_13058  0.032356       DFC_8-pcw_W1_M_13058
1         DFC_9-pcw_W1_M_12833  0.026008       DFC_9-pcw_W1_M_12833
2        DFC_12-pcw_W2_F_12835  0.010213      DFC_12-pcw_W2_F_12835
3        DFC_12-pcw_W2_F_12960  0.000000      DFC_12-pcw_W2_F_12960
4        DFC_12-pcw_W2_F_13060  0.000000      DFC_12-pcw_W2_F_13060
..                         ...       ...                        ...
726      DFC_19-pcw_W4_F_12885  0.000000      DFC_19-pcw_W4_F_12885
727      DFC_21-pcw_W4_M_12886  0.000000      DFC_21-pcw_W4_M_12886
728      DFC_24-pcw_W4_M_12288  0.000000      DFC_24-pcw_W4_M_12288
729      DFC_26-pcw_W4_F_12949  0.000000      DFC_26-pcw_W4_F_12949
730  DFC_37-pcw_W4_M_263195015  0.000000  DFC_37-pcw_W4_M_263195015

[731 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
[]
43
--------------------------------------------------------------------------------
                                      NFC	                                      
--------------------------------------------------------------------------------
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'M1C_12-pcw_W2_F_12835', 'M1C_12-pcw_W2_F_12960', 'M1C_12-pcw_W2_F_13060', 'M1C_13-pcw_W2_M_12820', 'M1C_13-pcw_W2_F_12834', 'M1C_13-pcw_W2_M_12888', 'M1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'M1C_21-pcw_W4_M_12886', 'M1C_24-pcw_W4_M_12288', 'M1C_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0     M1C-S1C_8-pcw_W1_M_13058  0.000000   M1C-S1C_8-pcw_W1_M_13058
1     M1C-S1C_9-pcw_W1_M_12833  0.024996   M1C-S1C_9-pcw_W1_M_12833
2        M1C_12-pcw_W2_F_12835  0.000000      M1C_12-pcw_W2_F_12835
3        M1C_12-pcw_W2_F_12960  0.000000      M1C_12-pcw_W2_F_12960
4        M1C_12-pcw_W2_F_13060  0.000000      M1C_12-pcw_W2_F_13060
..                         ...       ...                        ...
640  M1C-S1C_17-pcw_W3_F_12880  0.000000  M1C-S1C_17-pcw_W3_F_12880
641  M1C-S1C_19-pcw_W4_F_12885  0.000000  M1C-S1C_19-pcw_W4_F_12885
642      M1C_21-pcw_W4_M_12886  0.000000      M1C_21-pcw_W4_M_12886
643      M1C_24-pcw_W4_M_12288  0.000000      M1C_24-pcw_W4_M_12288
644  M1C_37-pcw_W4_M_263195015  0.000000  M1C_37-pcw_W4_M_263195015

[645 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'S1C_12-pcw_W2_F_12835', 'S1C_12-pcw_W2_F_12960', 'S1C_12-pcw_W2_F_13060', 'S1C_13-pcw_W2_M_12820', 'S1C_13-pcw_W2_F_12834', 'S1C_13-pcw_W2_M_12888', 'S1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'S1C_21-pcw_W4_M_12886', 'S1C_24-pcw_W4_M_12288', 'S1C_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0     M1C-S1C_8-pcw_W1_M_13058  0.000000   M1C-S1C_8-pcw_W1_M_13058
1     M1C-S1C_9-pcw_W1_M_12833  0.024996   M1C-S1C_9-pcw_W1_M_12833
2        S1C_12-pcw_W2_F_12835  0.000000      S1C_12-pcw_W2_F_12835
3        S1C_12-pcw_W2_F_12960  0.016167      S1C_12-pcw_W2_F_12960
4        S1C_12-pcw_W2_F_13060  0.000000      S1C_12-pcw_W2_F_13060
..                         ...       ...                        ...
640  M1C-S1C_17-pcw_W3_F_12880  0.000000  M1C-S1C_17-pcw_W3_F_12880
641  M1C-S1C_19-pcw_W4_F_12885  0.000000  M1C-S1C_19-pcw_W4_F_12885
642      S1C_21-pcw_W4_M_12886  0.000000      S1C_21-pcw_W4_M_12886
643      S1C_24-pcw_W4_M_12288  0.000000      S1C_24-pcw_W4_M_12288
644  S1C_37-pcw_W4_M_263195015  0.000000  S1C_37-pcw_W4_M_263195015

[645 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['IPC_12-pcw_W2_F_12835', 'IPC_12-pcw_W2_F_12960', 'IPC_12-pcw_W2_F_13060', 'IPC_13-pcw_W2_M_12820', 'IPC_13-pcw_W2_F_12834', 'IPC_13-pcw_W2_M_12888', 'IPC_16-pcw_W3_M_12287', 'IPC_16-pcw_W3_M_12837', 'IPC_16-pcw_W3_M_12879', 'IPC_17-pcw_W3_F_12880', 'IPC_19-pcw_W4_F_12885', 'IPC_21-pcw_W4_M_12886', 'IPC_24-pcw_W4_M_12288', 'IPC_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        IPC_12-pcw_W2_F_12835  0.011619      IPC_12-pcw_W2_F_12835
1        IPC_12-pcw_W2_F_12960  0.000000      IPC_12-pcw_W2_F_12960
2        IPC_12-pcw_W2_F_13060  0.000000      IPC_12-pcw_W2_F_13060
3        IPC_13-pcw_W2_M_12820  0.000000      IPC_13-pcw_W2_M_12820
4        IPC_13-pcw_W2_F_12834  0.000000      IPC_13-pcw_W2_F_12834
..                         ...       ...                        ...
597      IPC_17-pcw_W3_F_12880  0.000000      IPC_17-pcw_W3_F_12880
598      IPC_19-pcw_W4_F_12885  0.000000      IPC_19-pcw_W4_F_12885
599      IPC_21-pcw_W4_M_12886  0.000000      IPC_21-pcw_W4_M_12886
600      IPC_24-pcw_W4_M_12288  0.000000      IPC_24-pcw_W4_M_12288
601  IPC_37-pcw_W4_M_263195015  0.000000  IPC_37-pcw_W4_M_263195015

[602 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['A1C_12-pcw_W2_F_12835', 'A1C_12-pcw_W2_F_12960', 'A1C_12-pcw_W2_F_13060', 'A1C_13-pcw_W2_M_12820', 'A1C_13-pcw_W2_F_12834', 'A1C_13-pcw_W2_M_12888', 'A1C_16-pcw_W3_M_12287', 'A1C_16-pcw_W3_M_12837', 'A1C_16-pcw_W3_M_12879', 'A1C_17-pcw_W3_F_12880', 'A1C_19-pcw_W4_F_12885', 'A1C_24-pcw_W4_M_12288', 'A1C_25-pcw_W4_F_12948', 'A1C_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        A1C_12-pcw_W2_F_12835  0.010429      A1C_12-pcw_W2_F_12835
1        A1C_12-pcw_W2_F_12960  0.025298      A1C_12-pcw_W2_F_12960
2        A1C_12-pcw_W2_F_13060  0.000000      A1C_12-pcw_W2_F_13060
3        A1C_13-pcw_W2_M_12820  0.040545      A1C_13-pcw_W2_M_12820
4        A1C_13-pcw_W2_F_12834  0.000000      A1C_13-pcw_W2_F_12834
..                         ...       ...                        ...
597      A1C_17-pcw_W3_F_12880  0.000000      A1C_17-pcw_W3_F_12880
598      A1C_19-pcw_W4_F_12885  0.000000      A1C_19-pcw_W4_F_12885
599      A1C_24-pcw_W4_M_12288  0.000000      A1C_24-pcw_W4_M_12288
600      A1C_25-pcw_W4_F_12948  0.000000      A1C_25-pcw_W4_F_12948
601  A1C_37-pcw_W4_M_263195015  0.000000  A1C_37-pcw_W4_M_263195015

[602 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['STC_8-pcw_W1_M_13058', 'STC_12-pcw_W2_F_12835', 'STC_12-pcw_W2_F_12960', 'STC_13-pcw_W2_M_12820', 'STC_13-pcw_W2_F_12834', 'STC_16-pcw_W3_M_12287', 'STC_16-pcw_W3_M_12837', 'STC_16-pcw_W3_M_12879', 'STC_17-pcw_W3_F_12880', 'STC_19-pcw_W4_F_12885', 'STC_21-pcw_W4_M_12886', 'STC_24-pcw_W4_M_12288', 'STC_26-pcw_W4_F_12949', 'STC_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         STC_8-pcw_W1_M_13058  0.132294       STC_8-pcw_W1_M_13058
1        STC_12-pcw_W2_F_12835  0.010880      STC_12-pcw_W2_F_12835
2        STC_12-pcw_W2_F_12960  0.000000      STC_12-pcw_W2_F_12960
3        STC_13-pcw_W2_M_12820  0.011571      STC_13-pcw_W2_M_12820
4        STC_13-pcw_W2_F_12834  0.000000      STC_13-pcw_W2_F_12834
..                         ...       ...                        ...
597      STC_19-pcw_W4_F_12885  0.000000      STC_19-pcw_W4_F_12885
598      STC_21-pcw_W4_M_12886  0.000000      STC_21-pcw_W4_M_12886
599      STC_24-pcw_W4_M_12288  0.000000      STC_24-pcw_W4_M_12288
600      STC_26-pcw_W4_F_12949  0.000000      STC_26-pcw_W4_F_12949
601  STC_37-pcw_W4_M_263195015  0.000000  STC_37-pcw_W4_M_263195015

[602 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
[]
43
--------------------------------------------------------------------------------
                                      ITC	                                      
--------------------------------------------------------------------------------
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['V1C_12-pcw_W2_F_12835', 'V1C_12-pcw_W2_F_12960', 'V1C_12-pcw_W2_F_13060', 'V1C_13-pcw_W2_M_12820', 'V1C_13-pcw_W2_F_12834', 'V1C_13-pcw_W2_M_12888', 'V1C_16-pcw_W3_M_12287', 'V1C_16-pcw_W3_M_12837', 'V1C_16-pcw_W3_M_12879', 'V1C_17-pcw_W3_F_12880', 'V1C_19-pcw_W4_F_12885', 'V1C_21-pcw_W4_M_12886', 'V1C_24-pcw_W4_M_12288', 'V1C_26-pcw_W4_F_12949', 'V1C_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples  Values                 Conditions
0        V1C_12-pcw_W2_F_12835     0.0      V1C_12-pcw_W2_F_12835
1        V1C_12-pcw_W2_F_12960     0.0      V1C_12-pcw_W2_F_12960
2        V1C_12-pcw_W2_F_13060     0.0      V1C_12-pcw_W2_F_13060
3        V1C_13-pcw_W2_M_12820     0.0      V1C_13-pcw_W2_M_12820
4        V1C_13-pcw_W2_F_12834     0.0      V1C_13-pcw_W2_F_12834
..                         ...     ...                        ...
640      V1C_19-pcw_W4_F_12885     0.0      V1C_19-pcw_W4_F_12885
641      V1C_21-pcw_W4_M_12886     0.0      V1C_21-pcw_W4_M_12886
642      V1C_24-pcw_W4_M_12288     0.0      V1C_24-pcw_W4_M_12288
643      V1C_26-pcw_W4_F_12949     0.0      V1C_26-pcw_W4_F_12949
644  V1C_37-pcw_W4_M_263195015     0.0  V1C_37-pcw_W4_M_263195015

[645 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['HIP_8-pcw_W1_M_13058', 'HIP_9-pcw_W1_M_12833', 'HIP_12-pcw_W2_F_12835', 'HIP_12-pcw_W2_F_12960', 'HIP_12-pcw_W2_F_13060', 'HIP_13-pcw_W2_M_12820', 'HIP_13-pcw_W2_F_12834', 'HIP_13-pcw_W2_M_12888', 'HIP_16-pcw_W3_M_12837', 'HIP_16-pcw_W3_M_12879', 'HIP_17-pcw_W3_F_12880', 'HIP_19-pcw_W4_F_12885', 'HIP_21-pcw_W4_M_12886', 'HIP_24-pcw_W4_M_12288', 'HIP_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         HIP_8-pcw_W1_M_13058  0.061995       HIP_8-pcw_W1_M_13058
1         HIP_9-pcw_W1_M_12833  0.012682       HIP_9-pcw_W1_M_12833
2        HIP_12-pcw_W2_F_12835  0.000000      HIP_12-pcw_W2_F_12835
3        HIP_12-pcw_W2_F_12960  0.000000      HIP_12-pcw_W2_F_12960
4        HIP_12-pcw_W2_F_13060  0.027417      HIP_12-pcw_W2_F_13060
..                         ...       ...                        ...
640      HIP_17-pcw_W3_F_12880  0.000000      HIP_17-pcw_W3_F_12880
641      HIP_19-pcw_W4_F_12885  0.000000      HIP_19-pcw_W4_F_12885
642      HIP_21-pcw_W4_M_12886  0.000000      HIP_21-pcw_W4_M_12886
643      HIP_24-pcw_W4_M_12288  0.000000      HIP_24-pcw_W4_M_12288
644  HIP_37-pcw_W4_M_263195015  0.000000  HIP_37-pcw_W4_M_263195015

[645 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['AMY_8-pcw_W1_M_13058', 'AMY_9-pcw_W1_M_12833', 'AMY_12-pcw_W2_F_12835', 'AMY_12-pcw_W2_F_12960', 'AMY_12-pcw_W2_F_13060', 'AMY_13-pcw_W2_M_12820', 'AMY_13-pcw_W2_F_12834', 'AMY_13-pcw_W2_M_12888', 'AMY_16-pcw_W3_M_12837', 'AMY_16-pcw_W3_M_12879', 'AMY_17-pcw_W3_F_12880', 'AMY_21-pcw_W4_M_12886', 'AMY_24-pcw_W4_M_12288', 'AMY_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         AMY_8-pcw_W1_M_13058  0.029095       AMY_8-pcw_W1_M_13058
1         AMY_9-pcw_W1_M_12833  0.000000       AMY_9-pcw_W1_M_12833
2        AMY_12-pcw_W2_F_12835  0.013019      AMY_12-pcw_W2_F_12835
3        AMY_12-pcw_W2_F_12960  0.000000      AMY_12-pcw_W2_F_12960
4        AMY_12-pcw_W2_F_13060  0.000000      AMY_12-pcw_W2_F_13060
..                         ...       ...                        ...
597      AMY_16-pcw_W3_M_12879  0.000000      AMY_16-pcw_W3_M_12879
598      AMY_17-pcw_W3_F_12880  0.000000      AMY_17-pcw_W3_F_12880
599      AMY_21-pcw_W4_M_12886  0.000000      AMY_21-pcw_W4_M_12886
600      AMY_24-pcw_W4_M_12288  0.000000      AMY_24-pcw_W4_M_12288
601  AMY_37-pcw_W4_M_263195015  0.000000  AMY_37-pcw_W4_M_263195015

[602 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['STR_12-pcw_W2_F_12835', 'STR_12-pcw_W2_F_12960', 'STR_12-pcw_W2_F_13060', 'STR_13-pcw_W2_M_12820', 'STR_13-pcw_W2_F_12834', 'STR_13-pcw_W2_M_12888', 'STR_16-pcw_W3_M_12287', 'STR_16-pcw_W3_M_12837', 'STR_16-pcw_W3_M_12879', 'STR_17-pcw_W3_F_12880', 'STR_19-pcw_W4_F_12885', 'STR_21-pcw_W4_M_12886', 'STR_24-pcw_W4_M_12288', 'STR_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        STR_12-pcw_W2_F_12835  0.000000      STR_12-pcw_W2_F_12835
1        STR_12-pcw_W2_F_12960  0.011295      STR_12-pcw_W2_F_12960
2        STR_12-pcw_W2_F_13060  0.000000      STR_12-pcw_W2_F_13060
3        STR_13-pcw_W2_M_12820  0.000000      STR_13-pcw_W2_M_12820
4        STR_13-pcw_W2_F_12834  0.000000      STR_13-pcw_W2_F_12834
..                         ...       ...                        ...
597      STR_17-pcw_W3_F_12880  0.000000      STR_17-pcw_W3_F_12880
598      STR_19-pcw_W4_F_12885  0.000000      STR_19-pcw_W4_F_12885
599      STR_21-pcw_W4_M_12886  0.000000      STR_21-pcw_W4_M_12886
600      STR_24-pcw_W4_M_12288  0.000000      STR_24-pcw_W4_M_12288
601  STR_37-pcw_W4_M_263195015  0.000000  STR_37-pcw_W4_M_263195015

[602 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['MD_13-pcw_W2_F_12834', 'MD_16-pcw_W3_M_12287', 'MD_16-pcw_W3_M_12837', 'MD_16-pcw_W3_M_12879', 'MD_17-pcw_W3_F_12880', 'MD_19-pcw_W4_F_12885', 'MD_24-pcw_W4_M_12288', 'MD_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                      Samples  Values                Conditions
0        MD_13-pcw_W2_F_12834     0.0      MD_13-pcw_W2_F_12834
1        MD_16-pcw_W3_M_12287     0.0      MD_16-pcw_W3_M_12287
2        MD_16-pcw_W3_M_12837     0.0      MD_16-pcw_W3_M_12837
3        MD_16-pcw_W3_M_12879     0.0      MD_16-pcw_W3_M_12879
4        MD_17-pcw_W3_F_12880     0.0      MD_17-pcw_W3_F_12880
..                        ...     ...                       ...
339      MD_16-pcw_W3_M_12879     0.0      MD_16-pcw_W3_M_12879
340      MD_17-pcw_W3_F_12880     0.0      MD_17-pcw_W3_F_12880
341      MD_19-pcw_W4_F_12885     0.0      MD_19-pcw_W4_F_12885
342      MD_24-pcw_W4_M_12288     0.0      MD_24-pcw_W4_M_12288
343  MD_37-pcw_W4_M_263195015     0.0  MD_37-pcw_W4_M_263195015

[344 rows x 3 columns]
[1073, 1341, 2054, 3263, 3264, 3266, 3267, 3276, 3425, 3480, 3578, 3732, 4443, 5023, 5047, 5049, 5052, 5334, 5660, 5728, 5769, 6110, 6367, 7459, 7501, 7692, 11483, 11860, 12910, 13043, 13650, 13783, 14159, 14510, 15980, 16142, 16730, 16844, 17141, 18168, 24349, 24400, 35881]
['CBC_12-pcw_W2_F_13060', 'CBC_16-pcw_W3_M_12837', 'CBC_17-pcw_W3_F_12880', 'CBC_21-pcw_W4_F_12365', 'CBC_21-pcw_W4_M_12886', 'CBC_24-pcw_W4_M_12288', 'CBC_35-pcw_W4_F_12295', 'CBC_37-pcw_W4_M_263195015']
43
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        CBC_12-pcw_W2_F_13060  0.000000      CBC_12-pcw_W2_F_13060
1        CBC_16-pcw_W3_M_12837  0.000000      CBC_16-pcw_W3_M_12837
2        CBC_17-pcw_W3_F_12880  0.015429      CBC_17-pcw_W3_F_12880
3        CBC_21-pcw_W4_F_12365  0.000000      CBC_21-pcw_W4_F_12365
4        CBC_21-pcw_W4_M_12886  0.000000      CBC_21-pcw_W4_M_12886
..                         ...       ...                        ...
339      CBC_21-pcw_W4_F_12365  0.000000      CBC_21-pcw_W4_F_12365
340      CBC_21-pcw_W4_M_12886  0.000000      CBC_21-pcw_W4_M_12886
341      CBC_24-pcw_W4_M_12288  0.000000      CBC_24-pcw_W4_M_12288
342      CBC_35-pcw_W4_F_12295  0.000000      CBC_35-pcw_W4_F_12295
343  CBC_37-pcw_W4_M_263195015  0.000000  CBC_37-pcw_W4_M_263195015

[344 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['MFC_8-pcw_W1_M_13058', 'MFC_9-pcw_W1_M_12833', 'MFC_12-pcw_W2_F_12835', 'MFC_12-pcw_W2_F_13060', 'MFC_13-pcw_W2_M_12820', 'MFC_13-pcw_W2_F_12834', 'MFC_13-pcw_W2_M_12888', 'MFC_16-pcw_W3_M_12287', 'MFC_16-pcw_W3_M_12837', 'MFC_16-pcw_W3_M_12879', 'MFC_17-pcw_W3_F_12880', 'MFC_19-pcw_W4_F_12885', 'MFC_21-pcw_W4_M_12886', 'MFC_24-pcw_W4_M_12288', 'MFC_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         MFC_8-pcw_W1_M_13058  0.007508       MFC_8-pcw_W1_M_13058
1         MFC_9-pcw_W1_M_12833  0.053306       MFC_9-pcw_W1_M_12833
2        MFC_12-pcw_W2_F_12835  0.005942      MFC_12-pcw_W2_F_12835
3        MFC_12-pcw_W2_F_13060  0.005518      MFC_12-pcw_W2_F_13060
4        MFC_13-pcw_W2_M_12820  0.007602      MFC_13-pcw_W2_M_12820
..                         ...       ...                        ...
745      MFC_17-pcw_W3_F_12880  0.301602      MFC_17-pcw_W3_F_12880
746      MFC_19-pcw_W4_F_12885  0.679916      MFC_19-pcw_W4_F_12885
747      MFC_21-pcw_W4_M_12886  0.535459      MFC_21-pcw_W4_M_12886
748      MFC_24-pcw_W4_M_12288  0.749363      MFC_24-pcw_W4_M_12288
749  MFC_37-pcw_W4_M_263195015  6.056718  MFC_37-pcw_W4_M_263195015

[750 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['OFC_8-pcw_W1_M_13058', 'OFC_9-pcw_W1_M_12833', 'OFC_12-pcw_W2_F_12835', 'OFC_12-pcw_W2_F_12960', 'OFC_12-pcw_W2_F_13060', 'OFC_13-pcw_W2_M_12820', 'OFC_13-pcw_W2_F_12834', 'OFC_13-pcw_W2_M_12888', 'OFC_16-pcw_W3_M_12287', 'OFC_16-pcw_W3_M_12837', 'OFC_17-pcw_W3_F_12880', 'OFC_21-pcw_W4_M_12886', 'OFC_24-pcw_W4_M_12288', 'OFC_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         OFC_8-pcw_W1_M_13058  0.097615       OFC_8-pcw_W1_M_13058
1         OFC_9-pcw_W1_M_12833  0.031346       OFC_9-pcw_W1_M_12833
2        OFC_12-pcw_W2_F_12835  0.016257      OFC_12-pcw_W2_F_12835
3        OFC_12-pcw_W2_F_12960  0.013850      OFC_12-pcw_W2_F_12960
4        OFC_12-pcw_W2_F_13060  0.022450      OFC_12-pcw_W2_F_13060
..                         ...       ...                        ...
695      OFC_16-pcw_W3_M_12837  0.296723      OFC_16-pcw_W3_M_12837
696      OFC_17-pcw_W3_F_12880  0.429960      OFC_17-pcw_W3_F_12880
697      OFC_21-pcw_W4_M_12886  0.355161      OFC_21-pcw_W4_M_12886
698      OFC_24-pcw_W4_M_12288  0.510838      OFC_24-pcw_W4_M_12288
699  OFC_37-pcw_W4_M_263195015  5.532277  OFC_37-pcw_W4_M_263195015

[700 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['DFC_8-pcw_W1_M_13058', 'DFC_9-pcw_W1_M_12833', 'DFC_12-pcw_W2_F_12835', 'DFC_12-pcw_W2_F_12960', 'DFC_12-pcw_W2_F_13060', 'DFC_13-pcw_W2_M_12820', 'DFC_13-pcw_W2_F_12834', 'DFC_13-pcw_W2_M_12888', 'DFC_16-pcw_W3_M_12287', 'DFC_16-pcw_W3_M_12837', 'DFC_16-pcw_W3_M_12879', 'DFC_17-pcw_W3_F_12880', 'DFC_19-pcw_W4_F_12885', 'DFC_21-pcw_W4_M_12886', 'DFC_24-pcw_W4_M_12288', 'DFC_26-pcw_W4_F_12949', 'DFC_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         DFC_8-pcw_W1_M_13058  0.016205       DFC_8-pcw_W1_M_13058
1         DFC_9-pcw_W1_M_12833  0.057119       DFC_9-pcw_W1_M_12833
2        DFC_12-pcw_W2_F_12835  0.010105      DFC_12-pcw_W2_F_12835
3        DFC_12-pcw_W2_F_12960  0.038054      DFC_12-pcw_W2_F_12960
4        DFC_12-pcw_W2_F_13060  0.033411      DFC_12-pcw_W2_F_13060
..                         ...       ...                        ...
845      DFC_19-pcw_W4_F_12885  0.246193      DFC_19-pcw_W4_F_12885
846      DFC_21-pcw_W4_M_12886  0.485192      DFC_21-pcw_W4_M_12886
847      DFC_24-pcw_W4_M_12288  0.827281      DFC_24-pcw_W4_M_12288
848      DFC_26-pcw_W4_F_12949  2.868697      DFC_26-pcw_W4_F_12949
849  DFC_37-pcw_W4_M_263195015  5.877632  DFC_37-pcw_W4_M_263195015

[850 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
[]
50
--------------------------------------------------------------------------------
                                      NFC	                                      
--------------------------------------------------------------------------------
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'M1C_12-pcw_W2_F_12835', 'M1C_12-pcw_W2_F_12960', 'M1C_12-pcw_W2_F_13060', 'M1C_13-pcw_W2_M_12820', 'M1C_13-pcw_W2_F_12834', 'M1C_13-pcw_W2_M_12888', 'M1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'M1C_21-pcw_W4_M_12886', 'M1C_24-pcw_W4_M_12288', 'M1C_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0     M1C-S1C_8-pcw_W1_M_13058  0.041966   M1C-S1C_8-pcw_W1_M_13058
1     M1C-S1C_9-pcw_W1_M_12833  0.049129   M1C-S1C_9-pcw_W1_M_12833
2        M1C_12-pcw_W2_F_12835  0.015224      M1C_12-pcw_W2_F_12835
3        M1C_12-pcw_W2_F_12960  0.013113      M1C_12-pcw_W2_F_12960
4        M1C_12-pcw_W2_F_13060  0.010993      M1C_12-pcw_W2_F_13060
..                         ...       ...                        ...
745  M1C-S1C_17-pcw_W3_F_12880  0.314510  M1C-S1C_17-pcw_W3_F_12880
746  M1C-S1C_19-pcw_W4_F_12885  0.313320  M1C-S1C_19-pcw_W4_F_12885
747      M1C_21-pcw_W4_M_12886  0.516027      M1C_21-pcw_W4_M_12886
748      M1C_24-pcw_W4_M_12288  0.893080      M1C_24-pcw_W4_M_12288
749  M1C_37-pcw_W4_M_263195015  4.211396  M1C_37-pcw_W4_M_263195015

[750 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'S1C_12-pcw_W2_F_12835', 'S1C_12-pcw_W2_F_12960', 'S1C_12-pcw_W2_F_13060', 'S1C_13-pcw_W2_M_12820', 'S1C_13-pcw_W2_F_12834', 'S1C_13-pcw_W2_M_12888', 'S1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'S1C_21-pcw_W4_M_12886', 'S1C_24-pcw_W4_M_12288', 'S1C_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0     M1C-S1C_8-pcw_W1_M_13058  0.041966   M1C-S1C_8-pcw_W1_M_13058
1     M1C-S1C_9-pcw_W1_M_12833  0.049129   M1C-S1C_9-pcw_W1_M_12833
2        S1C_12-pcw_W2_F_12835  0.030405      S1C_12-pcw_W2_F_12835
3        S1C_12-pcw_W2_F_12960  0.031816      S1C_12-pcw_W2_F_12960
4        S1C_12-pcw_W2_F_13060  0.011544      S1C_12-pcw_W2_F_13060
..                         ...       ...                        ...
745  M1C-S1C_17-pcw_W3_F_12880  0.314510  M1C-S1C_17-pcw_W3_F_12880
746  M1C-S1C_19-pcw_W4_F_12885  0.313320  M1C-S1C_19-pcw_W4_F_12885
747      S1C_21-pcw_W4_M_12886  0.471947      S1C_21-pcw_W4_M_12886
748      S1C_24-pcw_W4_M_12288  0.727352      S1C_24-pcw_W4_M_12288
749  S1C_37-pcw_W4_M_263195015  4.235584  S1C_37-pcw_W4_M_263195015

[750 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['IPC_12-pcw_W2_F_12835', 'IPC_12-pcw_W2_F_12960', 'IPC_12-pcw_W2_F_13060', 'IPC_13-pcw_W2_M_12820', 'IPC_13-pcw_W2_F_12834', 'IPC_13-pcw_W2_M_12888', 'IPC_16-pcw_W3_M_12287', 'IPC_16-pcw_W3_M_12837', 'IPC_16-pcw_W3_M_12879', 'IPC_17-pcw_W3_F_12880', 'IPC_19-pcw_W4_F_12885', 'IPC_21-pcw_W4_M_12886', 'IPC_24-pcw_W4_M_12288', 'IPC_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        IPC_12-pcw_W2_F_12835  0.017249      IPC_12-pcw_W2_F_12835
1        IPC_12-pcw_W2_F_12960  0.032533      IPC_12-pcw_W2_F_12960
2        IPC_12-pcw_W2_F_13060  0.005537      IPC_12-pcw_W2_F_13060
3        IPC_13-pcw_W2_M_12820  0.007029      IPC_13-pcw_W2_M_12820
4        IPC_13-pcw_W2_F_12834  0.016556      IPC_13-pcw_W2_F_12834
..                         ...       ...                        ...
695      IPC_17-pcw_W3_F_12880  0.365343      IPC_17-pcw_W3_F_12880
696      IPC_19-pcw_W4_F_12885  0.498363      IPC_19-pcw_W4_F_12885
697      IPC_21-pcw_W4_M_12886  0.487875      IPC_21-pcw_W4_M_12886
698      IPC_24-pcw_W4_M_12288  0.573419      IPC_24-pcw_W4_M_12288
699  IPC_37-pcw_W4_M_263195015  4.714707  IPC_37-pcw_W4_M_263195015

[700 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['A1C_12-pcw_W2_F_12835', 'A1C_12-pcw_W2_F_12960', 'A1C_12-pcw_W2_F_13060', 'A1C_13-pcw_W2_M_12820', 'A1C_13-pcw_W2_F_12834', 'A1C_13-pcw_W2_M_12888', 'A1C_16-pcw_W3_M_12287', 'A1C_16-pcw_W3_M_12837', 'A1C_16-pcw_W3_M_12879', 'A1C_17-pcw_W3_F_12880', 'A1C_19-pcw_W4_F_12885', 'A1C_24-pcw_W4_M_12288', 'A1C_25-pcw_W4_F_12948', 'A1C_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        A1C_12-pcw_W2_F_12835  0.020702      A1C_12-pcw_W2_F_12835
1        A1C_12-pcw_W2_F_12960  0.049469      A1C_12-pcw_W2_F_12960
2        A1C_12-pcw_W2_F_13060  0.000000      A1C_12-pcw_W2_F_13060
3        A1C_13-pcw_W2_M_12820  0.010067      A1C_13-pcw_W2_M_12820
4        A1C_13-pcw_W2_F_12834  0.021944      A1C_13-pcw_W2_F_12834
..                         ...       ...                        ...
695      A1C_17-pcw_W3_F_12880  0.402572      A1C_17-pcw_W3_F_12880
696      A1C_19-pcw_W4_F_12885  0.421487      A1C_19-pcw_W4_F_12885
697      A1C_24-pcw_W4_M_12288  0.737359      A1C_24-pcw_W4_M_12288
698      A1C_25-pcw_W4_F_12948  1.257003      A1C_25-pcw_W4_F_12948
699  A1C_37-pcw_W4_M_263195015  3.967364  A1C_37-pcw_W4_M_263195015

[700 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['STC_8-pcw_W1_M_13058', 'STC_12-pcw_W2_F_12835', 'STC_12-pcw_W2_F_12960', 'STC_13-pcw_W2_M_12820', 'STC_13-pcw_W2_F_12834', 'STC_16-pcw_W3_M_12287', 'STC_16-pcw_W3_M_12837', 'STC_16-pcw_W3_M_12879', 'STC_17-pcw_W3_F_12880', 'STC_19-pcw_W4_F_12885', 'STC_21-pcw_W4_M_12886', 'STC_24-pcw_W4_M_12288', 'STC_26-pcw_W4_F_12949', 'STC_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         STC_8-pcw_W1_M_13058  0.037118       STC_8-pcw_W1_M_13058
1        STC_12-pcw_W2_F_12835  0.026869      STC_12-pcw_W2_F_12835
2        STC_12-pcw_W2_F_12960  0.068231      STC_12-pcw_W2_F_12960
3        STC_13-pcw_W2_M_12820  0.037281      STC_13-pcw_W2_M_12820
4        STC_13-pcw_W2_F_12834  0.053862      STC_13-pcw_W2_F_12834
..                         ...       ...                        ...
695      STC_19-pcw_W4_F_12885  0.512205      STC_19-pcw_W4_F_12885
696      STC_21-pcw_W4_M_12886  0.526800      STC_21-pcw_W4_M_12886
697      STC_24-pcw_W4_M_12288  0.835251      STC_24-pcw_W4_M_12288
698      STC_26-pcw_W4_F_12949  0.598472      STC_26-pcw_W4_F_12949
699  STC_37-pcw_W4_M_263195015  4.210539  STC_37-pcw_W4_M_263195015

[700 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
[]
50
--------------------------------------------------------------------------------
                                      ITC	                                      
--------------------------------------------------------------------------------
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['V1C_12-pcw_W2_F_12835', 'V1C_12-pcw_W2_F_12960', 'V1C_12-pcw_W2_F_13060', 'V1C_13-pcw_W2_M_12820', 'V1C_13-pcw_W2_F_12834', 'V1C_13-pcw_W2_M_12888', 'V1C_16-pcw_W3_M_12287', 'V1C_16-pcw_W3_M_12837', 'V1C_16-pcw_W3_M_12879', 'V1C_17-pcw_W3_F_12880', 'V1C_19-pcw_W4_F_12885', 'V1C_21-pcw_W4_M_12886', 'V1C_24-pcw_W4_M_12288', 'V1C_26-pcw_W4_F_12949', 'V1C_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        V1C_12-pcw_W2_F_12835  0.024866      V1C_12-pcw_W2_F_12835
1        V1C_12-pcw_W2_F_12960  0.045105      V1C_12-pcw_W2_F_12960
2        V1C_12-pcw_W2_F_13060  0.018811      V1C_12-pcw_W2_F_13060
3        V1C_13-pcw_W2_M_12820  0.031278      V1C_13-pcw_W2_M_12820
4        V1C_13-pcw_W2_F_12834  0.056489      V1C_13-pcw_W2_F_12834
..                         ...       ...                        ...
745      V1C_19-pcw_W4_F_12885  0.271462      V1C_19-pcw_W4_F_12885
746      V1C_21-pcw_W4_M_12886  0.332525      V1C_21-pcw_W4_M_12886
747      V1C_24-pcw_W4_M_12288  0.770478      V1C_24-pcw_W4_M_12288
748      V1C_26-pcw_W4_F_12949  1.753825      V1C_26-pcw_W4_F_12949
749  V1C_37-pcw_W4_M_263195015  2.893670  V1C_37-pcw_W4_M_263195015

[750 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['HIP_8-pcw_W1_M_13058', 'HIP_9-pcw_W1_M_12833', 'HIP_12-pcw_W2_F_12835', 'HIP_12-pcw_W2_F_12960', 'HIP_12-pcw_W2_F_13060', 'HIP_13-pcw_W2_M_12820', 'HIP_13-pcw_W2_F_12834', 'HIP_13-pcw_W2_M_12888', 'HIP_16-pcw_W3_M_12837', 'HIP_16-pcw_W3_M_12879', 'HIP_17-pcw_W3_F_12880', 'HIP_19-pcw_W4_F_12885', 'HIP_21-pcw_W4_M_12886', 'HIP_24-pcw_W4_M_12288', 'HIP_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         HIP_8-pcw_W1_M_13058  0.049748       HIP_8-pcw_W1_M_13058
1         HIP_9-pcw_W1_M_12833  0.049794       HIP_9-pcw_W1_M_12833
2        HIP_12-pcw_W2_F_12835  0.029423      HIP_12-pcw_W2_F_12835
3        HIP_12-pcw_W2_F_12960  0.074088      HIP_12-pcw_W2_F_12960
4        HIP_12-pcw_W2_F_13060  0.013721      HIP_12-pcw_W2_F_13060
..                         ...       ...                        ...
745      HIP_17-pcw_W3_F_12880  2.260488      HIP_17-pcw_W3_F_12880
746      HIP_19-pcw_W4_F_12885  3.877443      HIP_19-pcw_W4_F_12885
747      HIP_21-pcw_W4_M_12886  1.504750      HIP_21-pcw_W4_M_12886
748      HIP_24-pcw_W4_M_12288  2.526158      HIP_24-pcw_W4_M_12288
749  HIP_37-pcw_W4_M_263195015  4.370095  HIP_37-pcw_W4_M_263195015

[750 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['AMY_8-pcw_W1_M_13058', 'AMY_9-pcw_W1_M_12833', 'AMY_12-pcw_W2_F_12835', 'AMY_12-pcw_W2_F_12960', 'AMY_12-pcw_W2_F_13060', 'AMY_13-pcw_W2_M_12820', 'AMY_13-pcw_W2_F_12834', 'AMY_13-pcw_W2_M_12888', 'AMY_16-pcw_W3_M_12837', 'AMY_16-pcw_W3_M_12879', 'AMY_17-pcw_W3_F_12880', 'AMY_21-pcw_W4_M_12886', 'AMY_24-pcw_W4_M_12288', 'AMY_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         AMY_8-pcw_W1_M_13058  0.036139       AMY_8-pcw_W1_M_13058
1         AMY_9-pcw_W1_M_12833  0.000000       AMY_9-pcw_W1_M_12833
2        AMY_12-pcw_W2_F_12835  0.038735      AMY_12-pcw_W2_F_12835
3        AMY_12-pcw_W2_F_12960  0.032891      AMY_12-pcw_W2_F_12960
4        AMY_12-pcw_W2_F_13060  0.031886      AMY_12-pcw_W2_F_13060
..                         ...       ...                        ...
695      AMY_16-pcw_W3_M_12879  0.457997      AMY_16-pcw_W3_M_12879
696      AMY_17-pcw_W3_F_12880  1.009095      AMY_17-pcw_W3_F_12880
697      AMY_21-pcw_W4_M_12886  0.569795      AMY_21-pcw_W4_M_12886
698      AMY_24-pcw_W4_M_12288  1.195163      AMY_24-pcw_W4_M_12288
699  AMY_37-pcw_W4_M_263195015  3.434620  AMY_37-pcw_W4_M_263195015

[700 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['STR_12-pcw_W2_F_12835', 'STR_12-pcw_W2_F_12960', 'STR_12-pcw_W2_F_13060', 'STR_13-pcw_W2_M_12820', 'STR_13-pcw_W2_F_12834', 'STR_13-pcw_W2_M_12888', 'STR_16-pcw_W3_M_12287', 'STR_16-pcw_W3_M_12837', 'STR_16-pcw_W3_M_12879', 'STR_17-pcw_W3_F_12880', 'STR_19-pcw_W4_F_12885', 'STR_21-pcw_W4_M_12886', 'STR_24-pcw_W4_M_12288', 'STR_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        STR_12-pcw_W2_F_12835  0.007315      STR_12-pcw_W2_F_12835
1        STR_12-pcw_W2_F_12960  0.027890      STR_12-pcw_W2_F_12960
2        STR_12-pcw_W2_F_13060  0.000000      STR_12-pcw_W2_F_13060
3        STR_13-pcw_W2_M_12820  0.039576      STR_13-pcw_W2_M_12820
4        STR_13-pcw_W2_F_12834  0.040524      STR_13-pcw_W2_F_12834
..                         ...       ...                        ...
695      STR_17-pcw_W3_F_12880  0.852499      STR_17-pcw_W3_F_12880
696      STR_19-pcw_W4_F_12885  0.850981      STR_19-pcw_W4_F_12885
697      STR_21-pcw_W4_M_12886  0.716508      STR_21-pcw_W4_M_12886
698      STR_24-pcw_W4_M_12288  0.972895      STR_24-pcw_W4_M_12288
699  STR_37-pcw_W4_M_263195015  5.913759  STR_37-pcw_W4_M_263195015

[700 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['MD_13-pcw_W2_F_12834', 'MD_16-pcw_W3_M_12287', 'MD_16-pcw_W3_M_12837', 'MD_16-pcw_W3_M_12879', 'MD_17-pcw_W3_F_12880', 'MD_19-pcw_W4_F_12885', 'MD_24-pcw_W4_M_12288', 'MD_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                      Samples    Values                Conditions
0        MD_13-pcw_W2_F_12834  0.030165      MD_13-pcw_W2_F_12834
1        MD_16-pcw_W3_M_12287  0.634300      MD_16-pcw_W3_M_12287
2        MD_16-pcw_W3_M_12837  0.053262      MD_16-pcw_W3_M_12837
3        MD_16-pcw_W3_M_12879  0.015720      MD_16-pcw_W3_M_12879
4        MD_17-pcw_W3_F_12880  0.080221      MD_17-pcw_W3_F_12880
..                        ...       ...                       ...
395      MD_16-pcw_W3_M_12879  0.214232      MD_16-pcw_W3_M_12879
396      MD_17-pcw_W3_F_12880  0.644789      MD_17-pcw_W3_F_12880
397      MD_19-pcw_W4_F_12885  0.491785      MD_19-pcw_W4_F_12885
398      MD_24-pcw_W4_M_12288  0.756256      MD_24-pcw_W4_M_12288
399  MD_37-pcw_W4_M_263195015  6.841130  MD_37-pcw_W4_M_263195015

[400 rows x 3 columns]
[384, 2305, 3289, 3393, 3724, 4109, 4466, 4474, 4919, 5281, 5802, 6070, 6457, 6840, 6922, 6950, 6957, 7328, 7718, 8016, 8399, 8765, 8999, 9090, 9531, 9602, 10027, 10242, 10425, 10485, 10647, 10665, 11230, 11515, 11548, 11665, 11844, 12036, 12672, 12742, 13856, 15791, 15865, 16867, 17112, 17514, 17989, 20340, 20600, 39709]
['CBC_12-pcw_W2_F_13060', 'CBC_16-pcw_W3_M_12837', 'CBC_17-pcw_W3_F_12880', 'CBC_21-pcw_W4_F_12365', 'CBC_21-pcw_W4_M_12886', 'CBC_24-pcw_W4_M_12288', 'CBC_35-pcw_W4_F_12295', 'CBC_37-pcw_W4_M_263195015']
50
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        CBC_12-pcw_W2_F_13060  0.031164      CBC_12-pcw_W2_F_13060
1        CBC_16-pcw_W3_M_12837  0.325677      CBC_16-pcw_W3_M_12837
2        CBC_17-pcw_W3_F_12880  0.294037      CBC_17-pcw_W3_F_12880
3        CBC_21-pcw_W4_F_12365  0.061167      CBC_21-pcw_W4_F_12365
4        CBC_21-pcw_W4_M_12886  0.098491      CBC_21-pcw_W4_M_12886
..                         ...       ...                        ...
395      CBC_21-pcw_W4_F_12365  0.691361      CBC_21-pcw_W4_F_12365
396      CBC_21-pcw_W4_M_12886  0.967571      CBC_21-pcw_W4_M_12886
397      CBC_24-pcw_W4_M_12288  0.862033      CBC_24-pcw_W4_M_12288
398      CBC_35-pcw_W4_F_12295  2.395225      CBC_35-pcw_W4_F_12295
399  CBC_37-pcw_W4_M_263195015  4.810515  CBC_37-pcw_W4_M_263195015

[400 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['MFC_8-pcw_W1_M_13058', 'MFC_9-pcw_W1_M_12833', 'MFC_12-pcw_W2_F_12835', 'MFC_12-pcw_W2_F_13060', 'MFC_13-pcw_W2_M_12820', 'MFC_13-pcw_W2_F_12834', 'MFC_13-pcw_W2_M_12888', 'MFC_16-pcw_W3_M_12287', 'MFC_16-pcw_W3_M_12837', 'MFC_16-pcw_W3_M_12879', 'MFC_17-pcw_W3_F_12880', 'MFC_19-pcw_W4_F_12885', 'MFC_21-pcw_W4_M_12886', 'MFC_24-pcw_W4_M_12288', 'MFC_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          MFC_8-pcw_W1_M_13058  3.284979       MFC_8-pcw_W1_M_13058
1          MFC_9-pcw_W1_M_12833  2.815531       MFC_9-pcw_W1_M_12833
2         MFC_12-pcw_W2_F_12835  3.174598      MFC_12-pcw_W2_F_12835
3         MFC_12-pcw_W2_F_13060  0.641983      MFC_12-pcw_W2_F_13060
4         MFC_13-pcw_W2_M_12820  0.839461      MFC_13-pcw_W2_M_12820
...                         ...       ...                        ...
1105      MFC_17-pcw_W3_F_12880  4.695569      MFC_17-pcw_W3_F_12880
1106      MFC_19-pcw_W4_F_12885  5.103670      MFC_19-pcw_W4_F_12885
1107      MFC_21-pcw_W4_M_12886  5.541707      MFC_21-pcw_W4_M_12886
1108      MFC_24-pcw_W4_M_12288  4.496411      MFC_24-pcw_W4_M_12288
1109  MFC_37-pcw_W4_M_263195015  6.289881  MFC_37-pcw_W4_M_263195015

[1110 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['OFC_8-pcw_W1_M_13058', 'OFC_9-pcw_W1_M_12833', 'OFC_12-pcw_W2_F_12835', 'OFC_12-pcw_W2_F_12960', 'OFC_12-pcw_W2_F_13060', 'OFC_13-pcw_W2_M_12820', 'OFC_13-pcw_W2_F_12834', 'OFC_13-pcw_W2_M_12888', 'OFC_16-pcw_W3_M_12287', 'OFC_16-pcw_W3_M_12837', 'OFC_17-pcw_W3_F_12880', 'OFC_21-pcw_W4_M_12886', 'OFC_24-pcw_W4_M_12288', 'OFC_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          OFC_8-pcw_W1_M_13058  5.372783       OFC_8-pcw_W1_M_13058
1          OFC_9-pcw_W1_M_12833  3.613372       OFC_9-pcw_W1_M_12833
2         OFC_12-pcw_W2_F_12835  3.836819      OFC_12-pcw_W2_F_12835
3         OFC_12-pcw_W2_F_12960  0.875426      OFC_12-pcw_W2_F_12960
4         OFC_12-pcw_W2_F_13060  0.723493      OFC_12-pcw_W2_F_13060
...                         ...       ...                        ...
1031      OFC_16-pcw_W3_M_12837  3.596096      OFC_16-pcw_W3_M_12837
1032      OFC_17-pcw_W3_F_12880  4.210147      OFC_17-pcw_W3_F_12880
1033      OFC_21-pcw_W4_M_12886  5.656035      OFC_21-pcw_W4_M_12886
1034      OFC_24-pcw_W4_M_12288  4.992499      OFC_24-pcw_W4_M_12288
1035  OFC_37-pcw_W4_M_263195015  6.325579  OFC_37-pcw_W4_M_263195015

[1036 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['DFC_8-pcw_W1_M_13058', 'DFC_9-pcw_W1_M_12833', 'DFC_12-pcw_W2_F_12835', 'DFC_12-pcw_W2_F_12960', 'DFC_12-pcw_W2_F_13060', 'DFC_13-pcw_W2_M_12820', 'DFC_13-pcw_W2_F_12834', 'DFC_13-pcw_W2_M_12888', 'DFC_16-pcw_W3_M_12287', 'DFC_16-pcw_W3_M_12837', 'DFC_16-pcw_W3_M_12879', 'DFC_17-pcw_W3_F_12880', 'DFC_19-pcw_W4_F_12885', 'DFC_21-pcw_W4_M_12886', 'DFC_24-pcw_W4_M_12288', 'DFC_26-pcw_W4_F_12949', 'DFC_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          DFC_8-pcw_W1_M_13058  4.197448       DFC_8-pcw_W1_M_13058
1          DFC_9-pcw_W1_M_12833  2.309593       DFC_9-pcw_W1_M_12833
2         DFC_12-pcw_W2_F_12835  3.850865      DFC_12-pcw_W2_F_12835
3         DFC_12-pcw_W2_F_12960  0.785069      DFC_12-pcw_W2_F_12960
4         DFC_12-pcw_W2_F_13060  0.986155      DFC_12-pcw_W2_F_13060
...                         ...       ...                        ...
1253      DFC_19-pcw_W4_F_12885  5.009684      DFC_19-pcw_W4_F_12885
1254      DFC_21-pcw_W4_M_12886  5.439081      DFC_21-pcw_W4_M_12886
1255      DFC_24-pcw_W4_M_12288  4.727442      DFC_24-pcw_W4_M_12288
1256      DFC_26-pcw_W4_F_12949  5.601386      DFC_26-pcw_W4_F_12949
1257  DFC_37-pcw_W4_M_263195015  6.402107  DFC_37-pcw_W4_M_263195015

[1258 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
[]
74
--------------------------------------------------------------------------------
                                      NFC	                                      
--------------------------------------------------------------------------------
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'M1C_12-pcw_W2_F_12835', 'M1C_12-pcw_W2_F_12960', 'M1C_12-pcw_W2_F_13060', 'M1C_13-pcw_W2_M_12820', 'M1C_13-pcw_W2_F_12834', 'M1C_13-pcw_W2_M_12888', 'M1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'M1C_21-pcw_W4_M_12886', 'M1C_24-pcw_W4_M_12288', 'M1C_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0      M1C-S1C_8-pcw_W1_M_13058  3.473805   M1C-S1C_8-pcw_W1_M_13058
1      M1C-S1C_9-pcw_W1_M_12833  2.109657   M1C-S1C_9-pcw_W1_M_12833
2         M1C_12-pcw_W2_F_12835  3.874103      M1C_12-pcw_W2_F_12835
3         M1C_12-pcw_W2_F_12960  0.412837      M1C_12-pcw_W2_F_12960
4         M1C_12-pcw_W2_F_13060  0.632692      M1C_12-pcw_W2_F_13060
...                         ...       ...                        ...
1105  M1C-S1C_17-pcw_W3_F_12880  5.009501  M1C-S1C_17-pcw_W3_F_12880
1106  M1C-S1C_19-pcw_W4_F_12885  5.867112  M1C-S1C_19-pcw_W4_F_12885
1107      M1C_21-pcw_W4_M_12886  4.733579      M1C_21-pcw_W4_M_12886
1108      M1C_24-pcw_W4_M_12288  5.318903      M1C_24-pcw_W4_M_12288
1109  M1C_37-pcw_W4_M_263195015  6.312746  M1C_37-pcw_W4_M_263195015

[1110 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'S1C_12-pcw_W2_F_12835', 'S1C_12-pcw_W2_F_12960', 'S1C_12-pcw_W2_F_13060', 'S1C_13-pcw_W2_M_12820', 'S1C_13-pcw_W2_F_12834', 'S1C_13-pcw_W2_M_12888', 'S1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'S1C_21-pcw_W4_M_12886', 'S1C_24-pcw_W4_M_12288', 'S1C_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0      M1C-S1C_8-pcw_W1_M_13058  3.473805   M1C-S1C_8-pcw_W1_M_13058
1      M1C-S1C_9-pcw_W1_M_12833  2.109657   M1C-S1C_9-pcw_W1_M_12833
2         S1C_12-pcw_W2_F_12835  3.114384      S1C_12-pcw_W2_F_12835
3         S1C_12-pcw_W2_F_12960  0.734294      S1C_12-pcw_W2_F_12960
4         S1C_12-pcw_W2_F_13060  0.594500      S1C_12-pcw_W2_F_13060
...                         ...       ...                        ...
1105  M1C-S1C_17-pcw_W3_F_12880  5.009501  M1C-S1C_17-pcw_W3_F_12880
1106  M1C-S1C_19-pcw_W4_F_12885  5.867112  M1C-S1C_19-pcw_W4_F_12885
1107      S1C_21-pcw_W4_M_12886  5.279321      S1C_21-pcw_W4_M_12886
1108      S1C_24-pcw_W4_M_12288  5.367637      S1C_24-pcw_W4_M_12288
1109  S1C_37-pcw_W4_M_263195015  6.226608  S1C_37-pcw_W4_M_263195015

[1110 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['IPC_12-pcw_W2_F_12835', 'IPC_12-pcw_W2_F_12960', 'IPC_12-pcw_W2_F_13060', 'IPC_13-pcw_W2_M_12820', 'IPC_13-pcw_W2_F_12834', 'IPC_13-pcw_W2_M_12888', 'IPC_16-pcw_W3_M_12287', 'IPC_16-pcw_W3_M_12837', 'IPC_16-pcw_W3_M_12879', 'IPC_17-pcw_W3_F_12880', 'IPC_19-pcw_W4_F_12885', 'IPC_21-pcw_W4_M_12886', 'IPC_24-pcw_W4_M_12288', 'IPC_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0         IPC_12-pcw_W2_F_12835  3.497436      IPC_12-pcw_W2_F_12835
1         IPC_12-pcw_W2_F_12960  0.568253      IPC_12-pcw_W2_F_12960
2         IPC_12-pcw_W2_F_13060  0.777245      IPC_12-pcw_W2_F_13060
3         IPC_13-pcw_W2_M_12820  0.480799      IPC_13-pcw_W2_M_12820
4         IPC_13-pcw_W2_F_12834  0.507100      IPC_13-pcw_W2_F_12834
...                         ...       ...                        ...
1031      IPC_17-pcw_W3_F_12880  4.153229      IPC_17-pcw_W3_F_12880
1032      IPC_19-pcw_W4_F_12885  5.621041      IPC_19-pcw_W4_F_12885
1033      IPC_21-pcw_W4_M_12886  5.165740      IPC_21-pcw_W4_M_12886
1034      IPC_24-pcw_W4_M_12288  5.027073      IPC_24-pcw_W4_M_12288
1035  IPC_37-pcw_W4_M_263195015  6.302318  IPC_37-pcw_W4_M_263195015

[1036 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['A1C_12-pcw_W2_F_12835', 'A1C_12-pcw_W2_F_12960', 'A1C_12-pcw_W2_F_13060', 'A1C_13-pcw_W2_M_12820', 'A1C_13-pcw_W2_F_12834', 'A1C_13-pcw_W2_M_12888', 'A1C_16-pcw_W3_M_12287', 'A1C_16-pcw_W3_M_12837', 'A1C_16-pcw_W3_M_12879', 'A1C_17-pcw_W3_F_12880', 'A1C_19-pcw_W4_F_12885', 'A1C_24-pcw_W4_M_12288', 'A1C_25-pcw_W4_F_12948', 'A1C_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0         A1C_12-pcw_W2_F_12835  3.438876      A1C_12-pcw_W2_F_12835
1         A1C_12-pcw_W2_F_12960  0.636323      A1C_12-pcw_W2_F_12960
2         A1C_12-pcw_W2_F_13060  1.095997      A1C_12-pcw_W2_F_13060
3         A1C_13-pcw_W2_M_12820  1.189175      A1C_13-pcw_W2_M_12820
4         A1C_13-pcw_W2_F_12834  0.922500      A1C_13-pcw_W2_F_12834
...                         ...       ...                        ...
1031      A1C_17-pcw_W3_F_12880  4.754744      A1C_17-pcw_W3_F_12880
1032      A1C_19-pcw_W4_F_12885  5.065191      A1C_19-pcw_W4_F_12885
1033      A1C_24-pcw_W4_M_12288  4.960461      A1C_24-pcw_W4_M_12288
1034      A1C_25-pcw_W4_F_12948  5.487724      A1C_25-pcw_W4_F_12948
1035  A1C_37-pcw_W4_M_263195015  6.213068  A1C_37-pcw_W4_M_263195015

[1036 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['STC_8-pcw_W1_M_13058', 'STC_12-pcw_W2_F_12835', 'STC_12-pcw_W2_F_12960', 'STC_13-pcw_W2_M_12820', 'STC_13-pcw_W2_F_12834', 'STC_16-pcw_W3_M_12287', 'STC_16-pcw_W3_M_12837', 'STC_16-pcw_W3_M_12879', 'STC_17-pcw_W3_F_12880', 'STC_19-pcw_W4_F_12885', 'STC_21-pcw_W4_M_12886', 'STC_24-pcw_W4_M_12288', 'STC_26-pcw_W4_F_12949', 'STC_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          STC_8-pcw_W1_M_13058  4.525560       STC_8-pcw_W1_M_13058
1         STC_12-pcw_W2_F_12835  3.092616      STC_12-pcw_W2_F_12835
2         STC_12-pcw_W2_F_12960  0.628941      STC_12-pcw_W2_F_12960
3         STC_13-pcw_W2_M_12820  0.626428      STC_13-pcw_W2_M_12820
4         STC_13-pcw_W2_F_12834  0.934583      STC_13-pcw_W2_F_12834
...                         ...       ...                        ...
1031      STC_19-pcw_W4_F_12885  5.135285      STC_19-pcw_W4_F_12885
1032      STC_21-pcw_W4_M_12886  5.321797      STC_21-pcw_W4_M_12886
1033      STC_24-pcw_W4_M_12288  5.576975      STC_24-pcw_W4_M_12288
1034      STC_26-pcw_W4_F_12949  2.390774      STC_26-pcw_W4_F_12949
1035  STC_37-pcw_W4_M_263195015  6.551196  STC_37-pcw_W4_M_263195015

[1036 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
[]
74
--------------------------------------------------------------------------------
                                      ITC	                                      
--------------------------------------------------------------------------------
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['V1C_12-pcw_W2_F_12835', 'V1C_12-pcw_W2_F_12960', 'V1C_12-pcw_W2_F_13060', 'V1C_13-pcw_W2_M_12820', 'V1C_13-pcw_W2_F_12834', 'V1C_13-pcw_W2_M_12888', 'V1C_16-pcw_W3_M_12287', 'V1C_16-pcw_W3_M_12837', 'V1C_16-pcw_W3_M_12879', 'V1C_17-pcw_W3_F_12880', 'V1C_19-pcw_W4_F_12885', 'V1C_21-pcw_W4_M_12886', 'V1C_24-pcw_W4_M_12288', 'V1C_26-pcw_W4_F_12949', 'V1C_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0         V1C_12-pcw_W2_F_12835  3.478492      V1C_12-pcw_W2_F_12835
1         V1C_12-pcw_W2_F_12960  1.339429      V1C_12-pcw_W2_F_12960
2         V1C_12-pcw_W2_F_13060  0.789727      V1C_12-pcw_W2_F_13060
3         V1C_13-pcw_W2_M_12820  0.531536      V1C_13-pcw_W2_M_12820
4         V1C_13-pcw_W2_F_12834  0.903892      V1C_13-pcw_W2_F_12834
...                         ...       ...                        ...
1105      V1C_19-pcw_W4_F_12885  5.748135      V1C_19-pcw_W4_F_12885
1106      V1C_21-pcw_W4_M_12886  5.132945      V1C_21-pcw_W4_M_12886
1107      V1C_24-pcw_W4_M_12288  4.747033      V1C_24-pcw_W4_M_12288
1108      V1C_26-pcw_W4_F_12949  4.941985      V1C_26-pcw_W4_F_12949
1109  V1C_37-pcw_W4_M_263195015  6.438955  V1C_37-pcw_W4_M_263195015

[1110 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['HIP_8-pcw_W1_M_13058', 'HIP_9-pcw_W1_M_12833', 'HIP_12-pcw_W2_F_12835', 'HIP_12-pcw_W2_F_12960', 'HIP_12-pcw_W2_F_13060', 'HIP_13-pcw_W2_M_12820', 'HIP_13-pcw_W2_F_12834', 'HIP_13-pcw_W2_M_12888', 'HIP_16-pcw_W3_M_12837', 'HIP_16-pcw_W3_M_12879', 'HIP_17-pcw_W3_F_12880', 'HIP_19-pcw_W4_F_12885', 'HIP_21-pcw_W4_M_12886', 'HIP_24-pcw_W4_M_12288', 'HIP_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          HIP_8-pcw_W1_M_13058  4.377467       HIP_8-pcw_W1_M_13058
1          HIP_9-pcw_W1_M_12833  3.765243       HIP_9-pcw_W1_M_12833
2         HIP_12-pcw_W2_F_12835  3.656385      HIP_12-pcw_W2_F_12835
3         HIP_12-pcw_W2_F_12960  1.506994      HIP_12-pcw_W2_F_12960
4         HIP_12-pcw_W2_F_13060  2.407062      HIP_12-pcw_W2_F_13060
...                         ...       ...                        ...
1105      HIP_17-pcw_W3_F_12880  5.352350      HIP_17-pcw_W3_F_12880
1106      HIP_19-pcw_W4_F_12885  4.457435      HIP_19-pcw_W4_F_12885
1107      HIP_21-pcw_W4_M_12886  4.847682      HIP_21-pcw_W4_M_12886
1108      HIP_24-pcw_W4_M_12288  4.375224      HIP_24-pcw_W4_M_12288
1109  HIP_37-pcw_W4_M_263195015  4.729713  HIP_37-pcw_W4_M_263195015

[1110 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['AMY_8-pcw_W1_M_13058', 'AMY_9-pcw_W1_M_12833', 'AMY_12-pcw_W2_F_12835', 'AMY_12-pcw_W2_F_12960', 'AMY_12-pcw_W2_F_13060', 'AMY_13-pcw_W2_M_12820', 'AMY_13-pcw_W2_F_12834', 'AMY_13-pcw_W2_M_12888', 'AMY_16-pcw_W3_M_12837', 'AMY_16-pcw_W3_M_12879', 'AMY_17-pcw_W3_F_12880', 'AMY_21-pcw_W4_M_12886', 'AMY_24-pcw_W4_M_12288', 'AMY_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          AMY_8-pcw_W1_M_13058  2.622170       AMY_8-pcw_W1_M_13058
1          AMY_9-pcw_W1_M_12833  0.845903       AMY_9-pcw_W1_M_12833
2         AMY_12-pcw_W2_F_12835  1.051693      AMY_12-pcw_W2_F_12835
3         AMY_12-pcw_W2_F_12960  1.210129      AMY_12-pcw_W2_F_12960
4         AMY_12-pcw_W2_F_13060  1.256081      AMY_12-pcw_W2_F_13060
...                         ...       ...                        ...
1031      AMY_16-pcw_W3_M_12879  3.353997      AMY_16-pcw_W3_M_12879
1032      AMY_17-pcw_W3_F_12880  3.339671      AMY_17-pcw_W3_F_12880
1033      AMY_21-pcw_W4_M_12886  4.763857      AMY_21-pcw_W4_M_12886
1034      AMY_24-pcw_W4_M_12288  1.997733      AMY_24-pcw_W4_M_12288
1035  AMY_37-pcw_W4_M_263195015  5.431851  AMY_37-pcw_W4_M_263195015

[1036 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['STR_12-pcw_W2_F_12835', 'STR_12-pcw_W2_F_12960', 'STR_12-pcw_W2_F_13060', 'STR_13-pcw_W2_M_12820', 'STR_13-pcw_W2_F_12834', 'STR_13-pcw_W2_M_12888', 'STR_16-pcw_W3_M_12287', 'STR_16-pcw_W3_M_12837', 'STR_16-pcw_W3_M_12879', 'STR_17-pcw_W3_F_12880', 'STR_19-pcw_W4_F_12885', 'STR_21-pcw_W4_M_12886', 'STR_24-pcw_W4_M_12288', 'STR_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0         STR_12-pcw_W2_F_12835  0.948252      STR_12-pcw_W2_F_12835
1         STR_12-pcw_W2_F_12960  0.609956      STR_12-pcw_W2_F_12960
2         STR_12-pcw_W2_F_13060  0.774366      STR_12-pcw_W2_F_13060
3         STR_13-pcw_W2_M_12820  0.962454      STR_13-pcw_W2_M_12820
4         STR_13-pcw_W2_F_12834  0.471584      STR_13-pcw_W2_F_12834
...                         ...       ...                        ...
1031      STR_17-pcw_W3_F_12880  4.933134      STR_17-pcw_W3_F_12880
1032      STR_19-pcw_W4_F_12885  4.478964      STR_19-pcw_W4_F_12885
1033      STR_21-pcw_W4_M_12886  4.177044      STR_21-pcw_W4_M_12886
1034      STR_24-pcw_W4_M_12288  3.753650      STR_24-pcw_W4_M_12288
1035  STR_37-pcw_W4_M_263195015  4.695975  STR_37-pcw_W4_M_263195015

[1036 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['MD_13-pcw_W2_F_12834', 'MD_16-pcw_W3_M_12287', 'MD_16-pcw_W3_M_12837', 'MD_16-pcw_W3_M_12879', 'MD_17-pcw_W3_F_12880', 'MD_19-pcw_W4_F_12885', 'MD_24-pcw_W4_M_12288', 'MD_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                      Samples    Values                Conditions
0        MD_13-pcw_W2_F_12834  0.404743      MD_13-pcw_W2_F_12834
1        MD_16-pcw_W3_M_12287  1.293754      MD_16-pcw_W3_M_12287
2        MD_16-pcw_W3_M_12837  0.400524      MD_16-pcw_W3_M_12837
3        MD_16-pcw_W3_M_12879  1.118546      MD_16-pcw_W3_M_12879
4        MD_17-pcw_W3_F_12880  0.755182      MD_17-pcw_W3_F_12880
..                        ...       ...                       ...
587      MD_16-pcw_W3_M_12879  4.949760      MD_16-pcw_W3_M_12879
588      MD_17-pcw_W3_F_12880  5.943503      MD_17-pcw_W3_F_12880
589      MD_19-pcw_W4_F_12885  6.127394      MD_19-pcw_W4_F_12885
590      MD_24-pcw_W4_M_12288  6.175250      MD_24-pcw_W4_M_12288
591  MD_37-pcw_W4_M_263195015  4.287037  MD_37-pcw_W4_M_263195015

[592 rows x 3 columns]
[304, 651, 659, 735, 744, 795, 1033, 1199, 1222, 1647, 3138, 3418, 3624, 4179, 4208, 4239, 4416, 4452, 4608, 4992, 5113, 5270, 5287, 5795, 5920, 6047, 6460, 6728, 6924, 7277, 8252, 8558, 8676, 8752, 8786, 9256, 9330, 9523, 9598, 9931, 10177, 10249, 10264, 10535, 11325, 11805, 12407, 12517, 12683, 12728, 13190, 13256, 13340, 13564, 13637, 13655, 14060, 14157, 14369, 14876, 15038, 15066, 15563, 15612, 15662, 15669, 15734, 16183, 16303, 16915, 17143, 20302, 20561, 25925]
['CBC_12-pcw_W2_F_13060', 'CBC_16-pcw_W3_M_12837', 'CBC_17-pcw_W3_F_12880', 'CBC_21-pcw_W4_F_12365', 'CBC_21-pcw_W4_M_12886', 'CBC_24-pcw_W4_M_12288', 'CBC_35-pcw_W4_F_12295', 'CBC_37-pcw_W4_M_263195015']
74
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        CBC_12-pcw_W2_F_13060  1.776912      CBC_12-pcw_W2_F_13060
1        CBC_16-pcw_W3_M_12837  3.080056      CBC_16-pcw_W3_M_12837
2        CBC_17-pcw_W3_F_12880  3.193952      CBC_17-pcw_W3_F_12880
3        CBC_21-pcw_W4_F_12365  3.221126      CBC_21-pcw_W4_F_12365
4        CBC_21-pcw_W4_M_12886  4.201922      CBC_21-pcw_W4_M_12886
..                         ...       ...                        ...
587      CBC_21-pcw_W4_F_12365  2.130525      CBC_21-pcw_W4_F_12365
588      CBC_21-pcw_W4_M_12886  2.020747      CBC_21-pcw_W4_M_12886
589      CBC_24-pcw_W4_M_12288  2.965254      CBC_24-pcw_W4_M_12288
590      CBC_35-pcw_W4_F_12295  2.814185      CBC_35-pcw_W4_F_12295
591  CBC_37-pcw_W4_M_263195015  3.678598  CBC_37-pcw_W4_M_263195015

[592 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['MFC_8-pcw_W1_M_13058', 'MFC_9-pcw_W1_M_12833', 'MFC_12-pcw_W2_F_12835', 'MFC_12-pcw_W2_F_13060', 'MFC_13-pcw_W2_M_12820', 'MFC_13-pcw_W2_F_12834', 'MFC_13-pcw_W2_M_12888', 'MFC_16-pcw_W3_M_12287', 'MFC_16-pcw_W3_M_12837', 'MFC_16-pcw_W3_M_12879', 'MFC_17-pcw_W3_F_12880', 'MFC_19-pcw_W4_F_12885', 'MFC_21-pcw_W4_M_12886', 'MFC_24-pcw_W4_M_12288', 'MFC_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         MFC_8-pcw_W1_M_13058  0.451476       MFC_8-pcw_W1_M_13058
1         MFC_9-pcw_W1_M_12833  0.831099       MFC_9-pcw_W1_M_12833
2        MFC_12-pcw_W2_F_12835  0.346095      MFC_12-pcw_W2_F_12835
3        MFC_12-pcw_W2_F_13060  0.403080      MFC_12-pcw_W2_F_13060
4        MFC_13-pcw_W2_M_12820  0.334187      MFC_13-pcw_W2_M_12820
..                         ...       ...                        ...
895      MFC_17-pcw_W3_F_12880  2.074327      MFC_17-pcw_W3_F_12880
896      MFC_19-pcw_W4_F_12885  2.585697      MFC_19-pcw_W4_F_12885
897      MFC_21-pcw_W4_M_12886  1.436282      MFC_21-pcw_W4_M_12886
898      MFC_24-pcw_W4_M_12288  1.761179      MFC_24-pcw_W4_M_12288
899  MFC_37-pcw_W4_M_263195015  1.845261  MFC_37-pcw_W4_M_263195015

[900 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['OFC_8-pcw_W1_M_13058', 'OFC_9-pcw_W1_M_12833', 'OFC_12-pcw_W2_F_12835', 'OFC_12-pcw_W2_F_12960', 'OFC_12-pcw_W2_F_13060', 'OFC_13-pcw_W2_M_12820', 'OFC_13-pcw_W2_F_12834', 'OFC_13-pcw_W2_M_12888', 'OFC_16-pcw_W3_M_12287', 'OFC_16-pcw_W3_M_12837', 'OFC_17-pcw_W3_F_12880', 'OFC_21-pcw_W4_M_12886', 'OFC_24-pcw_W4_M_12288', 'OFC_37-pcw_W4_M_263195015']
60
                       Samples    Values                 Conditions
0         OFC_8-pcw_W1_M_13058  0.735473       OFC_8-pcw_W1_M_13058
1         OFC_9-pcw_W1_M_12833  0.364498       OFC_9-pcw_W1_M_12833
2        OFC_12-pcw_W2_F_12835  0.550980      OFC_12-pcw_W2_F_12835
3        OFC_12-pcw_W2_F_12960  0.345572      OFC_12-pcw_W2_F_12960
4        OFC_12-pcw_W2_F_13060  0.494155      OFC_12-pcw_W2_F_13060
..                         ...       ...                        ...
835      OFC_16-pcw_W3_M_12837  2.157575      OFC_16-pcw_W3_M_12837
836      OFC_17-pcw_W3_F_12880  2.477687      OFC_17-pcw_W3_F_12880
837      OFC_21-pcw_W4_M_12886  1.469855      OFC_21-pcw_W4_M_12886
838      OFC_24-pcw_W4_M_12288  2.133730      OFC_24-pcw_W4_M_12288
839  OFC_37-pcw_W4_M_263195015  1.952085  OFC_37-pcw_W4_M_263195015

[840 rows x 3 columns]
No handles with labels found to put in legend.
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['DFC_8-pcw_W1_M_13058', 'DFC_9-pcw_W1_M_12833', 'DFC_12-pcw_W2_F_12835', 'DFC_12-pcw_W2_F_12960', 'DFC_12-pcw_W2_F_13060', 'DFC_13-pcw_W2_M_12820', 'DFC_13-pcw_W2_F_12834', 'DFC_13-pcw_W2_M_12888', 'DFC_16-pcw_W3_M_12287', 'DFC_16-pcw_W3_M_12837', 'DFC_16-pcw_W3_M_12879', 'DFC_17-pcw_W3_F_12880', 'DFC_19-pcw_W4_F_12885', 'DFC_21-pcw_W4_M_12886', 'DFC_24-pcw_W4_M_12288', 'DFC_26-pcw_W4_F_12949', 'DFC_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                        Samples    Values                 Conditions
0          DFC_8-pcw_W1_M_13058  0.078981       DFC_8-pcw_W1_M_13058
1          DFC_9-pcw_W1_M_12833  0.306790       DFC_9-pcw_W1_M_12833
2         DFC_12-pcw_W2_F_12835  0.570094      DFC_12-pcw_W2_F_12835
3         DFC_12-pcw_W2_F_12960  0.441568      DFC_12-pcw_W2_F_12960
4         DFC_12-pcw_W2_F_13060  0.398380      DFC_12-pcw_W2_F_13060
...                         ...       ...                        ...
1015      DFC_19-pcw_W4_F_12885  2.881200      DFC_19-pcw_W4_F_12885
1016      DFC_21-pcw_W4_M_12886  1.850405      DFC_21-pcw_W4_M_12886
1017      DFC_24-pcw_W4_M_12288  1.808608      DFC_24-pcw_W4_M_12288
1018      DFC_26-pcw_W4_F_12949  1.421990      DFC_26-pcw_W4_F_12949
1019  DFC_37-pcw_W4_M_263195015  1.948983  DFC_37-pcw_W4_M_263195015

[1020 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
[]
60
--------------------------------------------------------------------------------
                                      NFC	                                      
--------------------------------------------------------------------------------
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'M1C_12-pcw_W2_F_12835', 'M1C_12-pcw_W2_F_12960', 'M1C_12-pcw_W2_F_13060', 'M1C_13-pcw_W2_M_12820', 'M1C_13-pcw_W2_F_12834', 'M1C_13-pcw_W2_M_12888', 'M1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'M1C_21-pcw_W4_M_12886', 'M1C_24-pcw_W4_M_12288', 'M1C_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0     M1C-S1C_8-pcw_W1_M_13058  0.782196   M1C-S1C_8-pcw_W1_M_13058
1     M1C-S1C_9-pcw_W1_M_12833  0.272342   M1C-S1C_9-pcw_W1_M_12833
2        M1C_12-pcw_W2_F_12835  0.390177      M1C_12-pcw_W2_F_12835
3        M1C_12-pcw_W2_F_12960  0.370315      M1C_12-pcw_W2_F_12960
4        M1C_12-pcw_W2_F_13060  0.335240      M1C_12-pcw_W2_F_13060
..                         ...       ...                        ...
895  M1C-S1C_17-pcw_W3_F_12880  1.945876  M1C-S1C_17-pcw_W3_F_12880
896  M1C-S1C_19-pcw_W4_F_12885  2.553411  M1C-S1C_19-pcw_W4_F_12885
897      M1C_21-pcw_W4_M_12886  2.306144      M1C_21-pcw_W4_M_12886
898      M1C_24-pcw_W4_M_12288  1.206903      M1C_24-pcw_W4_M_12288
899  M1C_37-pcw_W4_M_263195015  2.252401  M1C_37-pcw_W4_M_263195015

[900 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['M1C-S1C_8-pcw_W1_M_13058', 'M1C-S1C_9-pcw_W1_M_12833', 'S1C_12-pcw_W2_F_12835', 'S1C_12-pcw_W2_F_12960', 'S1C_12-pcw_W2_F_13060', 'S1C_13-pcw_W2_M_12820', 'S1C_13-pcw_W2_F_12834', 'S1C_13-pcw_W2_M_12888', 'S1C_16-pcw_W3_M_12837', 'M1C-S1C_16-pcw_W3_M_12879', 'M1C-S1C_17-pcw_W3_F_12880', 'M1C-S1C_19-pcw_W4_F_12885', 'S1C_21-pcw_W4_M_12886', 'S1C_24-pcw_W4_M_12288', 'S1C_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0     M1C-S1C_8-pcw_W1_M_13058  0.782196   M1C-S1C_8-pcw_W1_M_13058
1     M1C-S1C_9-pcw_W1_M_12833  0.272342   M1C-S1C_9-pcw_W1_M_12833
2        S1C_12-pcw_W2_F_12835  0.317244      S1C_12-pcw_W2_F_12835
3        S1C_12-pcw_W2_F_12960  0.222551      S1C_12-pcw_W2_F_12960
4        S1C_12-pcw_W2_F_13060  0.216270      S1C_12-pcw_W2_F_13060
..                         ...       ...                        ...
895  M1C-S1C_17-pcw_W3_F_12880  1.945876  M1C-S1C_17-pcw_W3_F_12880
896  M1C-S1C_19-pcw_W4_F_12885  2.553411  M1C-S1C_19-pcw_W4_F_12885
897      S1C_21-pcw_W4_M_12886  2.003517      S1C_21-pcw_W4_M_12886
898      S1C_24-pcw_W4_M_12288  1.243363      S1C_24-pcw_W4_M_12288
899  S1C_37-pcw_W4_M_263195015  2.214080  S1C_37-pcw_W4_M_263195015

[900 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['IPC_12-pcw_W2_F_12835', 'IPC_12-pcw_W2_F_12960', 'IPC_12-pcw_W2_F_13060', 'IPC_13-pcw_W2_M_12820', 'IPC_13-pcw_W2_F_12834', 'IPC_13-pcw_W2_M_12888', 'IPC_16-pcw_W3_M_12287', 'IPC_16-pcw_W3_M_12837', 'IPC_16-pcw_W3_M_12879', 'IPC_17-pcw_W3_F_12880', 'IPC_19-pcw_W4_F_12885', 'IPC_21-pcw_W4_M_12886', 'IPC_24-pcw_W4_M_12288', 'IPC_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        IPC_12-pcw_W2_F_12835  0.359436      IPC_12-pcw_W2_F_12835
1        IPC_12-pcw_W2_F_12960  0.433144      IPC_12-pcw_W2_F_12960
2        IPC_12-pcw_W2_F_13060  0.231608      IPC_12-pcw_W2_F_13060
3        IPC_13-pcw_W2_M_12820  0.316669      IPC_13-pcw_W2_M_12820
4        IPC_13-pcw_W2_F_12834  0.302795      IPC_13-pcw_W2_F_12834
..                         ...       ...                        ...
835      IPC_17-pcw_W3_F_12880  1.956243      IPC_17-pcw_W3_F_12880
836      IPC_19-pcw_W4_F_12885  2.792915      IPC_19-pcw_W4_F_12885
837      IPC_21-pcw_W4_M_12886  1.864394      IPC_21-pcw_W4_M_12886
838      IPC_24-pcw_W4_M_12288  1.445516      IPC_24-pcw_W4_M_12288
839  IPC_37-pcw_W4_M_263195015  2.035073  IPC_37-pcw_W4_M_263195015

[840 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['A1C_12-pcw_W2_F_12835', 'A1C_12-pcw_W2_F_12960', 'A1C_12-pcw_W2_F_13060', 'A1C_13-pcw_W2_M_12820', 'A1C_13-pcw_W2_F_12834', 'A1C_13-pcw_W2_M_12888', 'A1C_16-pcw_W3_M_12287', 'A1C_16-pcw_W3_M_12837', 'A1C_16-pcw_W3_M_12879', 'A1C_17-pcw_W3_F_12880', 'A1C_19-pcw_W4_F_12885', 'A1C_24-pcw_W4_M_12288', 'A1C_25-pcw_W4_F_12948', 'A1C_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        A1C_12-pcw_W2_F_12835  0.461471      A1C_12-pcw_W2_F_12835
1        A1C_12-pcw_W2_F_12960  0.389637      A1C_12-pcw_W2_F_12960
2        A1C_12-pcw_W2_F_13060  0.355013      A1C_12-pcw_W2_F_13060
3        A1C_13-pcw_W2_M_12820  0.583860      A1C_13-pcw_W2_M_12820
4        A1C_13-pcw_W2_F_12834  1.173916      A1C_13-pcw_W2_F_12834
..                         ...       ...                        ...
835      A1C_17-pcw_W3_F_12880  2.441355      A1C_17-pcw_W3_F_12880
836      A1C_19-pcw_W4_F_12885  2.524848      A1C_19-pcw_W4_F_12885
837      A1C_24-pcw_W4_M_12288  1.447642      A1C_24-pcw_W4_M_12288
838      A1C_25-pcw_W4_F_12948  1.982485      A1C_25-pcw_W4_F_12948
839  A1C_37-pcw_W4_M_263195015  2.149597  A1C_37-pcw_W4_M_263195015

[840 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['STC_8-pcw_W1_M_13058', 'STC_12-pcw_W2_F_12835', 'STC_12-pcw_W2_F_12960', 'STC_13-pcw_W2_M_12820', 'STC_13-pcw_W2_F_12834', 'STC_16-pcw_W3_M_12287', 'STC_16-pcw_W3_M_12837', 'STC_16-pcw_W3_M_12879', 'STC_17-pcw_W3_F_12880', 'STC_19-pcw_W4_F_12885', 'STC_21-pcw_W4_M_12886', 'STC_24-pcw_W4_M_12288', 'STC_26-pcw_W4_F_12949', 'STC_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         STC_8-pcw_W1_M_13058  1.735147       STC_8-pcw_W1_M_13058
1        STC_12-pcw_W2_F_12835  0.382171      STC_12-pcw_W2_F_12835
2        STC_12-pcw_W2_F_12960  0.328367      STC_12-pcw_W2_F_12960
3        STC_13-pcw_W2_M_12820  0.240810      STC_13-pcw_W2_M_12820
4        STC_13-pcw_W2_F_12834  0.608951      STC_13-pcw_W2_F_12834
..                         ...       ...                        ...
835      STC_19-pcw_W4_F_12885  2.584918      STC_19-pcw_W4_F_12885
836      STC_21-pcw_W4_M_12886  2.185517      STC_21-pcw_W4_M_12886
837      STC_24-pcw_W4_M_12288  1.886617      STC_24-pcw_W4_M_12288
838      STC_26-pcw_W4_F_12949  2.547309      STC_26-pcw_W4_F_12949
839  STC_37-pcw_W4_M_263195015  2.357544  STC_37-pcw_W4_M_263195015

[840 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
[]
60
--------------------------------------------------------------------------------
                                      ITC	                                      
--------------------------------------------------------------------------------
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['V1C_12-pcw_W2_F_12835', 'V1C_12-pcw_W2_F_12960', 'V1C_12-pcw_W2_F_13060', 'V1C_13-pcw_W2_M_12820', 'V1C_13-pcw_W2_F_12834', 'V1C_13-pcw_W2_M_12888', 'V1C_16-pcw_W3_M_12287', 'V1C_16-pcw_W3_M_12837', 'V1C_16-pcw_W3_M_12879', 'V1C_17-pcw_W3_F_12880', 'V1C_19-pcw_W4_F_12885', 'V1C_21-pcw_W4_M_12886', 'V1C_24-pcw_W4_M_12288', 'V1C_26-pcw_W4_F_12949', 'V1C_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        V1C_12-pcw_W2_F_12835  0.254427      V1C_12-pcw_W2_F_12835
1        V1C_12-pcw_W2_F_12960  0.111638      V1C_12-pcw_W2_F_12960
2        V1C_12-pcw_W2_F_13060  0.091462      V1C_12-pcw_W2_F_13060
3        V1C_13-pcw_W2_M_12820  0.186929      V1C_13-pcw_W2_M_12820
4        V1C_13-pcw_W2_F_12834  0.104818      V1C_13-pcw_W2_F_12834
..                         ...       ...                        ...
895      V1C_19-pcw_W4_F_12885  2.861490      V1C_19-pcw_W4_F_12885
896      V1C_21-pcw_W4_M_12886  1.647332      V1C_21-pcw_W4_M_12886
897      V1C_24-pcw_W4_M_12288  1.988301      V1C_24-pcw_W4_M_12288
898      V1C_26-pcw_W4_F_12949  1.588803      V1C_26-pcw_W4_F_12949
899  V1C_37-pcw_W4_M_263195015  2.578369  V1C_37-pcw_W4_M_263195015

[900 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['HIP_8-pcw_W1_M_13058', 'HIP_9-pcw_W1_M_12833', 'HIP_12-pcw_W2_F_12835', 'HIP_12-pcw_W2_F_12960', 'HIP_12-pcw_W2_F_13060', 'HIP_13-pcw_W2_M_12820', 'HIP_13-pcw_W2_F_12834', 'HIP_13-pcw_W2_M_12888', 'HIP_16-pcw_W3_M_12837', 'HIP_16-pcw_W3_M_12879', 'HIP_17-pcw_W3_F_12880', 'HIP_19-pcw_W4_F_12885', 'HIP_21-pcw_W4_M_12886', 'HIP_24-pcw_W4_M_12288', 'HIP_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         HIP_8-pcw_W1_M_13058  0.062314       HIP_8-pcw_W1_M_13058
1         HIP_9-pcw_W1_M_12833  0.221330       HIP_9-pcw_W1_M_12833
2        HIP_12-pcw_W2_F_12835  0.661620      HIP_12-pcw_W2_F_12835
3        HIP_12-pcw_W2_F_12960  0.829007      HIP_12-pcw_W2_F_12960
4        HIP_12-pcw_W2_F_13060  0.530073      HIP_12-pcw_W2_F_13060
..                         ...       ...                        ...
895      HIP_17-pcw_W3_F_12880  1.470888      HIP_17-pcw_W3_F_12880
896      HIP_19-pcw_W4_F_12885  1.898114      HIP_19-pcw_W4_F_12885
897      HIP_21-pcw_W4_M_12886  1.198301      HIP_21-pcw_W4_M_12886
898      HIP_24-pcw_W4_M_12288  1.460430      HIP_24-pcw_W4_M_12288
899  HIP_37-pcw_W4_M_263195015  1.916792  HIP_37-pcw_W4_M_263195015

[900 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['AMY_8-pcw_W1_M_13058', 'AMY_9-pcw_W1_M_12833', 'AMY_12-pcw_W2_F_12835', 'AMY_12-pcw_W2_F_12960', 'AMY_12-pcw_W2_F_13060', 'AMY_13-pcw_W2_M_12820', 'AMY_13-pcw_W2_F_12834', 'AMY_13-pcw_W2_M_12888', 'AMY_16-pcw_W3_M_12837', 'AMY_16-pcw_W3_M_12879', 'AMY_17-pcw_W3_F_12880', 'AMY_21-pcw_W4_M_12886', 'AMY_24-pcw_W4_M_12288', 'AMY_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0         AMY_8-pcw_W1_M_13058  4.842161       AMY_8-pcw_W1_M_13058
1         AMY_9-pcw_W1_M_12833  3.725392       AMY_9-pcw_W1_M_12833
2        AMY_12-pcw_W2_F_12835  3.790007      AMY_12-pcw_W2_F_12835
3        AMY_12-pcw_W2_F_12960  3.867737      AMY_12-pcw_W2_F_12960
4        AMY_12-pcw_W2_F_13060  3.212849      AMY_12-pcw_W2_F_13060
..                         ...       ...                        ...
835      AMY_16-pcw_W3_M_12879  2.513059      AMY_16-pcw_W3_M_12879
836      AMY_17-pcw_W3_F_12880  2.601035      AMY_17-pcw_W3_F_12880
837      AMY_21-pcw_W4_M_12886  1.941400      AMY_21-pcw_W4_M_12886
838      AMY_24-pcw_W4_M_12288  1.801591      AMY_24-pcw_W4_M_12288
839  AMY_37-pcw_W4_M_263195015  2.016011  AMY_37-pcw_W4_M_263195015

[840 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['STR_12-pcw_W2_F_12835', 'STR_12-pcw_W2_F_12960', 'STR_12-pcw_W2_F_13060', 'STR_13-pcw_W2_M_12820', 'STR_13-pcw_W2_F_12834', 'STR_13-pcw_W2_M_12888', 'STR_16-pcw_W3_M_12287', 'STR_16-pcw_W3_M_12837', 'STR_16-pcw_W3_M_12879', 'STR_17-pcw_W3_F_12880', 'STR_19-pcw_W4_F_12885', 'STR_21-pcw_W4_M_12886', 'STR_24-pcw_W4_M_12288', 'STR_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        STR_12-pcw_W2_F_12835  2.031381      STR_12-pcw_W2_F_12835
1        STR_12-pcw_W2_F_12960  4.551016      STR_12-pcw_W2_F_12960
2        STR_12-pcw_W2_F_13060  5.014710      STR_12-pcw_W2_F_13060
3        STR_13-pcw_W2_M_12820  4.621242      STR_13-pcw_W2_M_12820
4        STR_13-pcw_W2_F_12834  4.730272      STR_13-pcw_W2_F_12834
..                         ...       ...                        ...
835      STR_17-pcw_W3_F_12880  2.801094      STR_17-pcw_W3_F_12880
836      STR_19-pcw_W4_F_12885  2.789072      STR_19-pcw_W4_F_12885
837      STR_21-pcw_W4_M_12886  2.964861      STR_21-pcw_W4_M_12886
838      STR_24-pcw_W4_M_12288  1.997745      STR_24-pcw_W4_M_12288
839  STR_37-pcw_W4_M_263195015  2.319164  STR_37-pcw_W4_M_263195015

[840 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['MD_13-pcw_W2_F_12834', 'MD_16-pcw_W3_M_12287', 'MD_16-pcw_W3_M_12837', 'MD_16-pcw_W3_M_12879', 'MD_17-pcw_W3_F_12880', 'MD_19-pcw_W4_F_12885', 'MD_24-pcw_W4_M_12288', 'MD_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                      Samples    Values                Conditions
0        MD_13-pcw_W2_F_12834  0.321040      MD_13-pcw_W2_F_12834
1        MD_16-pcw_W3_M_12287  2.300713      MD_16-pcw_W3_M_12287
2        MD_16-pcw_W3_M_12837  0.112516      MD_16-pcw_W3_M_12837
3        MD_16-pcw_W3_M_12879  0.482130      MD_16-pcw_W3_M_12879
4        MD_17-pcw_W3_F_12880  0.730817      MD_17-pcw_W3_F_12880
..                        ...       ...                       ...
475      MD_16-pcw_W3_M_12879  1.682688      MD_16-pcw_W3_M_12879
476      MD_17-pcw_W3_F_12880  2.282026      MD_17-pcw_W3_F_12880
477      MD_19-pcw_W4_F_12885  2.618286      MD_19-pcw_W4_F_12885
478      MD_24-pcw_W4_M_12288  1.105299      MD_24-pcw_W4_M_12288
479  MD_37-pcw_W4_M_263195015  2.645900  MD_37-pcw_W4_M_263195015

[480 rows x 3 columns]
[136, 527, 882, 1457, 2517, 2846, 2942, 3186, 3240, 3749, 3882, 4455, 5056, 5994, 6702, 7193, 7335, 7365, 7479, 7904, 8575, 8638, 8745, 9218, 9743, 10057, 10308, 10382, 10445, 10795, 11152, 11154, 11269, 11425, 11531, 12438, 12464, 12845, 12908, 12973, 13014, 13718, 14091, 14112, 14122, 14974, 15122, 15364, 15409, 16082, 16451, 16849, 16880, 17066, 18331, 21195, 23702, 24403, 34356, 47336]
['CBC_12-pcw_W2_F_13060', 'CBC_16-pcw_W3_M_12837', 'CBC_17-pcw_W3_F_12880', 'CBC_21-pcw_W4_F_12365', 'CBC_21-pcw_W4_M_12886', 'CBC_24-pcw_W4_M_12288', 'CBC_35-pcw_W4_F_12295', 'CBC_37-pcw_W4_M_263195015']
60
No handles with labels found to put in legend.
                       Samples    Values                 Conditions
0        CBC_12-pcw_W2_F_13060  0.000000      CBC_12-pcw_W2_F_13060
1        CBC_16-pcw_W3_M_12837  0.101221      CBC_16-pcw_W3_M_12837
2        CBC_17-pcw_W3_F_12880  0.000000      CBC_17-pcw_W3_F_12880
3        CBC_21-pcw_W4_F_12365  0.051752      CBC_21-pcw_W4_F_12365
4        CBC_21-pcw_W4_M_12886  0.000000      CBC_21-pcw_W4_M_12886
..                         ...       ...                        ...
475      CBC_21-pcw_W4_F_12365  2.934922      CBC_21-pcw_W4_F_12365
476      CBC_21-pcw_W4_M_12886  2.764020      CBC_21-pcw_W4_M_12886
477      CBC_24-pcw_W4_M_12288  2.208514      CBC_24-pcw_W4_M_12288
478      CBC_35-pcw_W4_F_12295  4.017169      CBC_35-pcw_W4_F_12295
479  CBC_37-pcw_W4_M_263195015  2.951623  CBC_37-pcw_W4_M_263195015

[480 rows x 3 columns]
In [206]:
###############################################################################
#                                                                             #
#    This program is free software: you can redistribute it and/or modify     #
#    it under the terms of the GNU General Public License as published by     #
#    the Free Software Foundation, either version 3 of the License, or        #
#    (at your option) any later version.                                      #
#                                                                             #
#    This program is distributed in the hope that it will be useful,          #
#    but WITHOUT ANY WARRANTY; without even the implied warranty of           #
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the            #
#    GNU General Public License for more details.                             #
#                                                                             #
#    You should have received a copy of the GNU General Public License        #
#    along with this program. If not, see <http://www.gnu.org/licenses/>.     #
#                                                                             #
###############################################################################

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

from sciviso import Vis
from statannot import add_stat_annotation


class Violinplot(Vis):

    def __init__(self, df: pd.DataFrame, x: object, y: object, title='', xlabel='', ylabel='', hue=None, order=None,
                 hue_order=None, showfliers=False, add_dots=False, add_stats=False, stat_method='Mann-Whitney',
                 figsize=(1.5, 1.5), title_font_size=8,
                 label_font_size=6, title_font_weight=700):
        super().__init__(df, figsize=figsize, title_font_size=title_font_size, label_font_size=label_font_size,
                         title_font_weight=title_font_weight)
        self.df = df
        self.x = x
        self.y = y
        self.xlabel = xlabel
        self.ylabel = ylabel
        self.title = title
        self.hue = hue
        self.order = order
        self.hue_order = hue_order
        self.showfliers = showfliers
        self.add_dots = add_dots
        self.add_stats = add_stats
        self.stat_method = stat_method

    def plot(self):
        x, y, hue, order, hue_order = self.x, self.y, self.hue, self.order, self.hue_order
        if not isinstance(self.x, str) and not isinstance(self.y, str):
            vis_df = pd.DataFrame()
            vis_df['x'] = x
            vis_df['y'] = y
            x = 'x'
            y = 'y'
            if self.hue is not None:
                vis_df['colour'] = self.hue
                hue = 'colour'
            if order is None:
                order = list(set(vis_df['x'].values))
                order.sort()
        else:
            vis_df = self.df
        # set the orders
        if hue_order is None and hue is not None:
            hue_order = list(set(vis_df[hue].values))
            hue_order.sort()
        if order is None:
            order = list(set(vis_df[x].values))
            order.sort()

        ax = sns.violinplot(data=vis_df, x=x, y=y, hue=hue, hue_order=hue_order, order=order, palette=self.palette,
                            showfliers=self.showfliers)
        if self.add_dots:
            ax = sns.stripplot(data=vis_df, x=x, y=y, hue_order=hue_order, order=order, alpha=0.9, s =1, color='.2')
        if self.add_stats:
            # Add all pairs in the order if the box pairs is none

            pairs = []
            box_pairs = []
            for i in order:
                for j in order:
                    if i != j:
                        # Ensure we don't get duplicates
                        pair = f'{i}{j}' if i < j else f'{j}{i}'
                        if pair not in pairs:
                            box_pairs.append((i, j))
                            pairs.append(pair)
            # Add stats annotation
            add_stat_annotation(ax, data=vis_df, x=x, y=y, order=order,
                                box_pairs=box_pairs,
                                test=self.stat_method, text_format='star', loc='inside', verbose=2)
        ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')
        ax.tick_params(labelsize=self.label_font_size)
        plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=self.label_font_size)
        self.add_labels()
        self.set_ax_params(ax)
        return ax
In [197]:
def plot_mm_gene_boxplot(df, title, cluster_id, cols, idxs, ylim=10, colours=None):
    idxs = idxs
    boxplot = Boxplot(df, 'ensembl_gene_id', cols[0])
    box_df = boxplot.format_data_for_boxplot(df, cols, gene_name, df[gene_name].values[idxs])

    boxplot = Violinplot(box_df, "Conditions", "Values", add_stats=True, add_dots=True, figsize=(1.0,1.0), 
                       order=cols, title_font_size=7, label_font_size=6)
    boxplot.palette = colours

    ax = boxplot.plot()
    plt.title(title)
    #ax.set_ylim(0, 12)

    save_fig(f'{title}')
    
    plt.show()

    
def plot_hu_gene_boxplot(df, title, cluster_id, cols, idxs, ylim=10, colours=None):
    idxs = idxs
    boxplot = Boxplot(df, 'ensembl_gene_id', cols[0])
    box_df = boxplot.format_data_for_boxplot(df, cols, 'ensembl_gene_id', df['ensembl_gene_id'].values[idxs])

    boxplot = Violinplot(box_df, "Conditions", "Values", add_stats=True, add_dots=True, figsize=(1.0,1.0), 
                       order=cols, title_font_size=7, label_font_size=6)
    boxplot.palette = colours

    ax = boxplot.plot()
    plt.title(title)
    #ax.set_ylim(0, 12)

    save_fig(f'{title}')
    
    plt.show()
    
def mouse_gene_tst(df, gene_lst, label, brain_tissue='', time_m=''):
    """ Plot the genes """
    wt_cols = [gene_name]
    for c in df.columns:
        if 'wt' in c and 'merged' not in c and brain_tissue in c:
            wt_cols.append(c)

    # Plot gene boxplot
    wt_df = df[wt_cols]

    g2_idxs = []
    for i, g in enumerate(df[gene_name].values):
        if g in genes:
            g2_idxs.append(i)

    plot_mm_gene_boxplot(wt_df, f'{label} in time {brain_tissue} mouse', i, ['11', '13', '15', '18'], g2_idxs, 
                         0, colours=[e11_colour, e13_colour, e15_colour, e18_colour])

    wt_cols = [gene_name]
    for c in df.columns:
        if 'merged' not in c and brain_tissue in c and time_m in c:
            wt_cols.append(c)

    # Plot gene boxplot
    wt_df = df[wt_cols]
    plot_mm_gene_boxplot(wt_df, f'{label}  wt-ko in {brain_tissue} mouse', i, ['wt', 'ko'], g2_idxs, 0, 
                        colours=[wt_colour, ko_colour])

    """ Do the same for the brain genes """
    g2_idxs = []
    for i, g in enumerate(df[gene_name].values):
        if g in genes:
            g2_idxs.append(i)

    wt_cols = [gene_name]
    for c in df.columns:
        if 'wt' in c and 'merged' not in c and '11' not in c:
            wt_cols.append(c)
    wt_df = df[wt_cols]       
    plot_mm_gene_boxplot(wt_df, f'Test for {label} in mouse', i, ['fb', 'mb', 'hb', 'sc'], 
                         g2_idxs, 0, colours=[fb_colour, mb_colour, hb_colour, sc_colour])



def human_gene_tst(df, gene_lst, label, human_brain_tissue='', human_time=''):
    # Make a new DF
    human_emb_a = pd.DataFrame()
    tissue_c = ['DFC', 'HIP', 'STR', 'CBC']
    human_emb_a['ensembl_gene_id'] = human_rm['ensembl_gene_id'].values
    for c in human_emb.columns:
        if human_brain_tissue in c:
            human_emb_a[c] = np.log2(human_emb[c].values + 1)

    genes = human_emb['gene_symbol'].values

    g2m_genes = []
    for g in gene_lst:
        ens = gene_name_to_ens_id.get(g)
        if ens is not None:
            # Now try get human
            hu = mouse_human_dict.get(ens)
            if hu:
                g2m_genes.append(hu)
                #mm_grps.append(g)
    print(g2m_genes)
    g2_idxs = []
    for i, g in enumerate(human_emb['ensembl_gene_id'].values):
        if g in g2m_genes:
            g2_idxs.append(i)

    # Boxplot of genes for Time points in OFC
    plot_hu_gene_boxplot(human_emb_a, f'Test for {label} in ages {human_brain_tissue} of Human embryos',
                         i, ['W1', 'W2', 'W3', 'W4'], 
                         g2_idxs, 0, colours=[e11_colour, e13_colour, e15_colour, e18_colour])

    human_emb_a = pd.DataFrame()
    human_emb_a['ensembl_gene_id'] = human_rm['ensembl_gene_id'].values
    tissue_c = ['DFC', 'HIP', 'STR', 'CBC']
    human_emb_nz = human_emb.fillna(0)
    
    for c in human_emb.columns:
        if c.split('_')[0] in tissue_c and human_time in c:
            human_emb_a[c] = np.nan_to_num(np.log2(human_emb_nz[c].values + 1))
    genes = human_emb['gene_symbol'].values
    g2_idxs = []
    for i, g in enumerate(human_emb['ensembl_gene_id'].values):
        if g in g2m_genes:
            g2_idxs.append(i)

    plot_hu_gene_boxplot(human_emb_a, f'Test for {label} in tissue of Human embryos', i, tissue_c, g2_idxs, 0, 
                        colours=[fb_colour, mb_colour, hb_colour])


    
In [209]:
genes =['Ccnb1',
'Cdc25c',
'E2f1',
'Ccna2',
'Ccnd1']
human_gene_tst(df, genes, 'Const-aff_Pro-prolif', 'DFC')
mouse_gene_tst(df, genes, 'Const-aff_Pro-prolif', '', '13')
['ENSG00000134057', 'ENSG00000101412', 'ENSG00000145386', 'ENSG00000110092']
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

W1 v.s. W2: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.942e-04 U_stat=1.920e+02
W2 v.s. W3: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=9.423e-02 U_stat=1.040e+02
W3 v.s. W4: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.260e-01 U_stat=2.330e+02
W1 v.s. W3: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=6.050e-04 U_stat=1.280e+02
W2 v.s. W4: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=2.670e+02
W1 v.s. W4: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.864e-04 U_stat=1.580e+02
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

DFC v.s. HIP: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.263e-07 U_stat=9.110e+02
HIP v.s. STR: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.752e-04 U_stat=2.437e+03
STR v.s. CBC: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.916e-04 U_stat=4.160e+02
DFC v.s. STR: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.597e-01 U_stat=1.501e+03
HIP v.s. CBC: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=9.680e+02
DFC v.s. CBC: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=7.164e-07 U_stat=3.710e+02
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

11 v.s. 13: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=3.924e-05 U_stat=1.269e+03
13 v.s. 15: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.646e-01 U_stat=9.840e+02
15 v.s. 18: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.694e-01 U_stat=9.520e+02
11 v.s. 15: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=7.820e-09 U_stat=1.431e+03
13 v.s. 18: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.097e-02 U_stat=1.104e+03
11 v.s. 18: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.832e-11 U_stat=1.519e+03
No handles with labels found to put in legend.
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

wt v.s. ko: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.909e-02 U_stat=1.005e+03
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

fb v.s. mb: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.959e-01 U_stat=5.950e+02
mb v.s. hb: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=3.007e-01 U_stat=5.830e+02
hb v.s. sc: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=3.890e+02
fb v.s. hb: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.783e-04 U_stat=7.260e+02
mb v.s. sc: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=5.250e+02
fb v.s. sc: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.377e-03 U_stat=6.790e+02
No handles with labels found to put in legend.
In [193]:
fb_vae_genes = ['Nr2e1', 'Fezf2', 'Helt', 'Gsx2', 'Emx1', 'Tfap2c', 'Fgf8', 'Eomes', 'Cldn4', 'Alx1'] 
human_gene_tst(df, fb_vae_genes, 'Const-aff_FB-markers_DFC', 'DFC', '')
mouse_gene_tst(df, fb_vae_genes, 'Const-aff_FB-markers_E13', '', '13')
['ENSG00000112333', 'ENSG00000153266', 'ENSG00000187821', 'ENSG00000180613', 'ENSG00000135638', 'ENSG00000163508']
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

W1 v.s. W2: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=2.630e+02
W2 v.s. W3: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=4.040e+02
W3 v.s. W4: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=4.110e+02
W1 v.s. W3: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=1.825e+02
W2 v.s. W4: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=5.590e+02
W1 v.s. W4: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.227e-01 U_stat=2.550e+02
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

DFC v.s. HIP: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.095e-01 U_stat=3.685e+03
HIP v.s. STR: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.302e-05 U_stat=5.353e+03
STR v.s. CBC: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=7.095e-07 U_stat=3.134e+03
DFC v.s. STR: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=6.205e-01 U_stat=4.879e+03
HIP v.s. CBC: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=3.775e-08 U_stat=3.451e+03
DFC v.s. CBC: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.862e-05 U_stat=3.543e+03
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

11 v.s. 13: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=3.924e-05 U_stat=1.269e+03
13 v.s. 15: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.646e-01 U_stat=9.840e+02
15 v.s. 18: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.694e-01 U_stat=9.520e+02
11 v.s. 15: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=7.820e-09 U_stat=1.431e+03
13 v.s. 18: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.097e-02 U_stat=1.104e+03
11 v.s. 18: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.832e-11 U_stat=1.519e+03
No handles with labels found to put in legend.
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

wt v.s. ko: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.909e-02 U_stat=1.005e+03
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

fb v.s. mb: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.959e-01 U_stat=5.950e+02
mb v.s. hb: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=3.007e-01 U_stat=5.830e+02
hb v.s. sc: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=3.890e+02
fb v.s. hb: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.783e-04 U_stat=7.260e+02
mb v.s. sc: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=5.250e+02
fb v.s. sc: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.377e-03 U_stat=6.790e+02
No handles with labels found to put in legend.

Get smallest distances between samples

Here we want to print out the sample that has the smallest normalise SSQ difference between genes

In [40]:
wt_cols = [c for c in df.columns if 'wt' in c and 'merged' in c]
ko_cols = [c for c in df.columns if 'ko' in c and 'merged' in c]
ssq_dists = []
u.dp(["All difference:"])
for wc in ko_cols:
    smallest_diff = 10000000000
    mst_sim = None
    for kc in wt_cols:
        x_diff = np.sqrt(np.sum((df_all[wc].values - df_all[kc].values)**2, axis=0))
        print(f'{wc.split("_")[0]},{kc.split("_")[0]},{x_diff}')
        if x_diff < smallest_diff:
            smallest_diff = x_diff
            mst_sim = kc.split("_")[0]
            
### Now let's just print out the smalles t diff
u.dp(["Smallest difference:"])
for wc in ko_cols:
    smallest_diff = 10000000000
    mst_sim = None
    for kc in wt_cols:
        x_diff = np.sqrt(np.sum((df_all[wc].values - df_all[kc].values)**2, axis=0))
        if x_diff < smallest_diff:
            smallest_diff = x_diff
            mst_sim = kc.split("_")[0]
    print(f'{wc.split("_")[0]},{mst_sim},{smallest_diff}')
--------------------------------------------------------------------------------
                                All difference:	                                
--------------------------------------------------------------------------------
ko11fb,wt11fb,46.13041217141976
ko11fb,wt13fb,96.31498537846748
ko11fb,wt15fb,126.17521819740459
ko11fb,wt18fb,158.15170028653253
ko11fb,wt11mb,32.68888649920045
ko11fb,wt13mb,123.24215198500161
ko11fb,wt15mb,152.15502397144562
ko11fb,wt18mb,173.64756692424598
ko11fb,wt11hb,52.74528561931896
ko11fb,wt13hb,157.98424020311603
ko11fb,wt15hb,175.52118288533703
ko11fb,wt18hb,184.4663742350005
ko11fb,wt11sc,72.84236630973075
ko11fb,wt13sc,149.49809716892403
ko11fb,wt15sc,155.1793045187712
ko11fb,wt18sc,186.02043234719997
ko13fb,wt11fb,145.8941922169742
ko13fb,wt13fb,67.84849788293374
ko13fb,wt15fb,77.39694302354529
ko13fb,wt18fb,89.6881937422115
ko13fb,wt11mb,127.15664000179015
ko13fb,wt13mb,67.77767343459935
ko13fb,wt15mb,83.77920656498978
ko13fb,wt18mb,101.74735210224104
ko13fb,wt11hb,119.70631784203455
ko13fb,wt13hb,82.61527700345748
ko13fb,wt15hb,106.33532459031616
ko13fb,wt18hb,113.16604989323818
ko13fb,wt11sc,131.23383808820358
ko13fb,wt13sc,75.97639735859156
ko13fb,wt15sc,98.58541602749087
ko13fb,wt18sc,114.99078097938364
ko15fb,wt11fb,168.97036515244676
ko15fb,wt13fb,104.3944656269549
ko15fb,wt15fb,76.72342444455278
ko15fb,wt18fb,70.49758900828148
ko15fb,wt11mb,150.6479230360108
ko15fb,wt13mb,94.74999931905626
ko15fb,wt15mb,60.33440375046574
ko15fb,wt18mb,71.57391224341826
ko15fb,wt11hb,142.37970801137155
ko15fb,wt13hb,94.05166824421276
ko15fb,wt15hb,79.68650964025527
ko15fb,wt18hb,78.66878774914635
ko15fb,wt11sc,152.67323789299581
ko15fb,wt13sc,89.73496768954588
ko15fb,wt15sc,78.20605589743606
ko15fb,wt18sc,83.70658517945307
ko18fb,wt11fb,191.28984795000025
ko18fb,wt13fb,131.82605126584775
ko18fb,wt15fb,103.07672980761625
ko18fb,wt18fb,81.84638668927123
ko18fb,wt11mb,175.7573782572159
ko18fb,wt13mb,122.46087001977595
ko18fb,wt15mb,80.00344055624122
ko18fb,wt18mb,72.74911697444946
ko18fb,wt11hb,166.527698168119
ko18fb,wt13hb,115.81394569494304
ko18fb,wt15hb,89.10180657743132
ko18fb,wt18hb,66.04132378258159
ko18fb,wt11sc,175.58269826311286
ko18fb,wt13sc,109.31963493867639
ko18fb,wt15sc,85.85842330311874
ko18fb,wt18sc,72.74405964775009
ko11mb,wt11fb,70.39389117831261
ko11mb,wt13fb,94.20637354541272
ko11mb,wt15fb,120.5009198445979
ko11mb,wt18fb,150.07802949179248
ko11mb,wt11mb,35.62931391926339
ko11mb,wt13mb,109.62856659514476
ko11mb,wt15mb,138.88599369681316
ko11mb,wt18mb,161.71151249084977
ko11mb,wt11hb,29.97232850692718
ko11mb,wt13hb,140.8450500081643
ko11mb,wt15hb,161.0929002385474
ko11mb,wt18hb,170.68673952882244
ko11mb,wt11sc,57.73274811287853
ko11mb,wt13sc,131.31227008629165
ko11mb,wt15sc,139.33603158703124
ko11mb,wt18sc,172.22535250240117
ko13mb,wt11fb,164.43876727099612
ko13mb,wt13fb,92.35854456295282
ko13mb,wt15fb,91.89789174265387
ko13mb,wt18fb,94.35226595559703
ko13mb,wt11mb,140.44651135986996
ko13mb,wt13mb,63.253560479254624
ko13mb,wt15mb,71.33162738018679
ko13mb,wt18mb,90.06439796155111
ko13mb,wt11hb,129.18049574938223
ko13mb,wt13hb,63.41251228132235
ko13mb,wt15hb,90.1055872776663
ko13mb,wt18hb,99.27060653813892
ko13mb,wt11sc,139.41950064349496
ko13mb,wt13sc,56.335123721914535
ko13mb,wt15sc,87.2115968339671
ko13mb,wt18sc,99.31695509723083
ko15mb,wt11fb,178.29574602088758
ko15mb,wt13fb,117.34912743204427
ko15mb,wt15fb,90.82686172805792
ko15mb,wt18fb,80.29687187564295
ko15mb,wt11mb,157.67076799432556
ko15mb,wt13mb,96.45690408751757
ko15mb,wt15mb,54.13465121463266
ko15mb,wt18mb,65.21394789281688
ko15mb,wt11hb,148.26663316431979
ko15mb,wt13hb,90.79415867692082
ko15mb,wt15hb,71.80085002525686
ko15mb,wt18hb,70.98723090279856
ko15mb,wt11sc,157.6108722459524
ko15mb,wt13sc,85.57641683854897
ko15mb,wt15sc,71.03324044384954
ko15mb,wt18sc,73.18677653632881
ko18mb,wt11fb,199.23542707242208
ko18mb,wt13fb,140.42878672490045
ko18mb,wt15fb,110.50674553557177
ko18mb,wt18fb,88.25497649399966
ko18mb,wt11mb,180.7668712289844
ko18mb,wt13mb,122.41672885597488
ko18mb,wt15mb,76.24660572895219
ko18mb,wt18mb,63.248358369567875
ko18mb,wt11hb,171.35175148108831
ko18mb,wt13hb,112.61900176471796
ko18mb,wt15hb,79.86923118156386
ko18mb,wt18hb,57.58886671691453
ko18mb,wt11sc,180.41985262871827
ko18mb,wt13sc,108.50150770960654
ko18mb,wt15sc,85.73369660214298
ko18mb,wt18sc,61.70203819375935
ko11hb,wt11fb,52.08597542504151
ko11hb,wt13fb,92.18825929164254
ko11hb,wt15fb,121.51356533931283
ko11hb,wt18fb,153.52004766429533
ko11hb,wt11mb,40.00758477243406
ko11hb,wt13mb,118.3731787938469
ko11hb,wt15mb,147.86020243517433
ko11hb,wt18mb,168.95013041327977
ko11hb,wt11hb,36.10814400271775
ko11hb,wt13hb,148.23495778167344
ko11hb,wt15hb,167.9845912575635
ko11hb,wt18hb,177.85992340377868
ko11hb,wt11sc,59.035082099942905
ko11hb,wt13sc,139.76997295934697
ko11hb,wt15sc,148.17357748281546
ko11hb,wt18sc,179.29336107542656
ko13hb,wt11fb,175.536040778772
ko13hb,wt13fb,107.90709591161469
ko13hb,wt15fb,102.74663545987363
ko13hb,wt18fb,96.30563345393823
ko13hb,wt11mb,155.41834619163777
ko13hb,wt13mb,87.77138729353369
ko13hb,wt15mb,77.89268979779217
ko13hb,wt18mb,90.21176619769211
ko13hb,wt11hb,140.7831615514921
ko13hb,wt13hb,74.51049892567289
ko13hb,wt15hb,87.99801550660989
ko13hb,wt18hb,85.2596593266265
ko13hb,wt11sc,148.94330071258435
ko13hb,wt13sc,58.83105957528475
ko13hb,wt15sc,76.0064518363831
ko13hb,wt18sc,86.48044167139982
ko15hb,wt11fb,187.9562358353821
ko15hb,wt13fb,128.4914455069699
ko15hb,wt15fb,100.21157925068545
ko15hb,wt18fb,85.66771875482961
ko15hb,wt11mb,167.70198134019904
ko15hb,wt13mb,107.54759458166806
ko15hb,wt15mb,63.150469788123324
ko15hb,wt18mb,66.89073929855964
ko15hb,wt11hb,156.1644425804308
ko15hb,wt13hb,92.6156462086282
ko15hb,wt15hb,66.09185487250873
ko15hb,wt18hb,60.73879848105781
ko15hb,wt11sc,164.44637079448802
ko15hb,wt13sc,86.45552842943044
ko15hb,wt15sc,68.49950954694795
ko15hb,wt18sc,62.25782477008093
ko18hb,wt11fb,205.24846789826412
ko18hb,wt13fb,150.91620056171553
ko18hb,wt15fb,122.67294160361848
ko18hb,wt18fb,101.30937564966494
ko18hb,wt11mb,188.2996280360275
ko18hb,wt13mb,135.09815495962278
ko18hb,wt15mb,90.9553515672788
ko18hb,wt18mb,79.01574026323541
ko18hb,wt11hb,176.71279266531812
ko18hb,wt13hb,120.26106977421333
ko18hb,wt15hb,84.93377734687152
ko18hb,wt18hb,54.09282069632413
ko18hb,wt11sc,183.97281947250937
ko18hb,wt13sc,112.3961742161104
ko18hb,wt15sc,84.80881152182062
ko18hb,wt18sc,57.79423449802449
ko11sc,wt11fb,90.41120662260553
ko11sc,wt13fb,107.42302960059246
ko11sc,wt15fb,129.73660170481702
ko11sc,wt18fb,156.6366104416174
ko11sc,wt11mb,72.28491030036136
ko11sc,wt13mb,121.1750213820497
ko11sc,wt15mb,147.1151471965951
ko11sc,wt18mb,168.0839441324853
ko11sc,wt11hb,51.45421492109428
ko11sc,wt13hb,140.08744641913296
ko11sc,wt15hb,161.63462827947802
ko11sc,wt18hb,172.07105976217645
ko11sc,wt11sc,24.45802204214485
ko11sc,wt13sc,124.57481799520966
ko11sc,wt15sc,134.268688629612
ko11sc,wt18sc,170.71576905027092
ko13sc,wt11fb,177.7791185747306
ko13sc,wt13fb,110.41565899558313
ko13sc,wt15fb,105.1053257156928
ko13sc,wt18fb,99.71901580709526
ko13sc,wt11mb,156.6912674759723
ko13sc,wt13mb,87.49758256277696
ko13sc,wt15mb,81.0415722338746
ko13sc,wt18mb,92.92270830657138
ko13sc,wt11hb,142.22591623273166
ko13sc,wt13hb,66.69282748110359
ko13sc,wt15hb,85.68712451071573
ko13sc,wt18hb,91.12373375139968
ko13sc,wt11sc,146.51209212991927
ko13sc,wt13sc,37.10442755059509
ko13sc,wt15sc,73.61719654486488
ko13sc,wt18sc,83.68672133876571
ko15sc,wt11fb,186.72557902699072
ko15sc,wt13fb,128.32769430207688
ko15sc,wt15fb,101.04423463073827
ko15sc,wt18fb,87.87979351642133
ko15sc,wt11mb,166.90214908733589
ko15sc,wt13mb,108.58155392321537
ko15sc,wt15mb,67.27509264866337
ko15sc,wt18mb,71.06953371372605
ko15sc,wt11hb,154.56069814125954
ko15sc,wt13hb,89.47974568613199
ko15sc,wt15hb,63.2906801326022
ko15sc,wt18hb,60.39143136154344
ko15sc,wt11sc,159.68128085866948
ko15sc,wt13sc,76.34950785703731
ko15sc,wt15sc,56.43238751190871
ko15sc,wt18sc,51.52878022144902
ko18sc,wt11fb,205.10824498672667
ko18sc,wt13fb,149.22390088830247
ko18sc,wt15fb,120.67403106812293
ko18sc,wt18fb,98.84976103306896
ko18sc,wt11mb,187.3147383150811
ko18sc,wt13mb,132.2209514459519
ko18sc,wt15mb,88.0918260031381
ko18sc,wt18mb,75.13942045906998
ko18sc,wt11hb,175.73651900490304
ko18sc,wt13hb,113.057355233022
ko18sc,wt15hb,75.50186303995245
ko18sc,wt18hb,51.24663307547252
ko18sc,wt11sc,180.45910162627004
ko18sc,wt13sc,103.31816927456056
ko18sc,wt15sc,73.69528029282989
ko18sc,wt18sc,39.24134771234434
--------------------------------------------------------------------------------
                             Smallest difference:	                              
--------------------------------------------------------------------------------
ko11fb,wt11mb,32.68888649920045
ko13fb,wt13mb,67.77767343459935
ko15fb,wt15mb,60.33440375046574
ko18fb,wt18hb,66.04132378258159
ko11mb,wt11hb,29.97232850692718
ko13mb,wt13sc,56.335123721914535
ko15mb,wt15mb,54.13465121463266
ko18mb,wt18hb,57.58886671691453
ko11hb,wt11hb,36.10814400271775
ko13hb,wt13sc,58.83105957528475
ko15hb,wt18hb,60.73879848105781
ko18hb,wt18hb,54.09282069632413
ko11sc,wt11sc,24.45802204214485
ko13sc,wt13sc,37.10442755059509
ko15sc,wt18sc,51.52878022144902
ko18sc,wt18sc,39.24134771234434
In [41]:
# Have a look at the TFs and their targets for regulons (i.e. does grp2 have TFs that tagret group3?)
regs = pd.read_csv(supp_dir + 'regulons_mm10_A-B.csv')
reg_tfs = set(regs['tf'].values)
group_labels = {1: 'Prolifferation', 2: 'Posterior', 3: 'Immune response', 4: 'Development', 5: 'Forebrain'}

for i in range(1, n_clusters):
    genes = df_training[gene_name].values[vae_set_idxs[i]]
    # Flag the TFs in this group, we'll then test these to see if we have lots of the targets in another group
    tfs = list(reg_tfs & set(genes)) 
    # Now we want to go through each of the other groups and record how many were targetted
    u.dp([group_labels[i], f'\n{len(tfs)} Tfs: ', ', '.join(tfs)])

    for j in range(1, n_clusters):
        genes_targets = df_training[gene_name].values[vae_set_idxs[j]]
        total_targets = 0
        for tf in tfs:
            targets = set(regs[regs['tf'] == tf]['target'].values)
            total_targets += len(list(targets & set(genes_targets)))
            if len(list(targets & set(genes_targets))) > 0:
                print(f'{tf} -->', ', '.join(list(targets & set(genes_targets))))
        print(f'--------------- Total for {group_labels[i]} targeting {group_labels[j]}: {total_targets} -------------')

        
#regs = pd.read_csv(supp_dir + 'regulons_mm10_A-B-C-D.csv')
regs[regs['tf'] == 'Cdkn1b'] #Tbx3, mesoderm markeds Hand1, gata2
#df_sig = pd.read_csv(f'{input_dir}df-significant_epi-2500_20210124.csv')
for g in regs[regs['target'] == 'Foxm1']['tf'].values:
    if len(df_training[df_training[gene_name] == g]) > 0:
        print(g, '\n')
        #print(df_sig[df_sig[gene_name] == g])
--------------------------------------------------------------------------------
       Prolifferation	
6 Tfs: 	Foxm1, Tal1, Tcf7l2, Mybl2, E2f1, Twist1	        
--------------------------------------------------------------------------------
Foxm1 --> Cenpf, Cdc6, Ccnb1, Bmi1, Cdc25c, Cenpa, Ccnd1, Aurkb
Tcf7l2 --> Ccnd1
E2f1 --> Foxm1, Rrm2, Gmnn, Kif2c, Cdc6, Ccna2, Bmi1, Cdca7, Mybl2, Ect2, Mcm10, Cdc45, Tgfb2, Top2a, Ccnd1, Uhrf1
Twist1 --> Nr2f1
--------------- Total for Prolifferation targeting Prolifferation: 26 -------------
E2f1 --> Hoxb9, Pax2
--------------- Total for Prolifferation targeting Posterior: 2 -------------
Foxm1 --> Cenpa, Cdc25c, Aurkb, Cdc6
Tal1 --> C3ar1
E2f1 --> Cdc6, Sp110, Ect2, Slc14a2, Mcm10, Cdc45, Oca2, Top2a
--------------- Total for Prolifferation targeting Immune response: 13 -------------
Foxm1 --> Cdkn1a
E2f1 --> Pax2, Hoxb9, Cdkn1a
Twist1 --> Cdkn1a
--------------- Total for Prolifferation targeting Development: 5 -------------
E2f1 --> Mybl2, Trp73
Twist1 --> Cldn7
--------------- Total for Prolifferation targeting Forebrain: 3 -------------
--------------------------------------------------------------------------------
               Posterior	
5 Tfs: 	Pitx1, Tfap2a, Wt1, Lhx3, Pax8	               
--------------------------------------------------------------------------------
Tfap2a --> Igfbp5, Ccnb1
Pax8 --> E2f1
--------------- Total for Posterior targeting Prolifferation: 3 -------------
Tfap2a --> Hoxa4
Wt1 --> Pax2
Pax8 --> Wt1
--------------- Total for Posterior targeting Posterior: 3 -------------
--------------- Total for Posterior targeting Immune response: 0 -------------
Tfap2a --> Igfbp5, Cdkn1a
Wt1 --> Pax2, Cdkn1a
--------------- Total for Posterior targeting Development: 4 -------------
--------------- Total for Posterior targeting Forebrain: 0 -------------
--------------------------------------------------------------------------------
                           Immune response	
0 Tfs: 		                           
--------------------------------------------------------------------------------
--------------- Total for Immune response targeting Prolifferation: 0 -------------
--------------- Total for Immune response targeting Posterior: 0 -------------
--------------- Total for Immune response targeting Immune response: 0 -------------
--------------- Total for Immune response targeting Development: 0 -------------
--------------- Total for Immune response targeting Forebrain: 0 -------------
--------------------------------------------------------------------------------
                           Development	
1 Tfs: 	Pax8	                           
--------------------------------------------------------------------------------
Pax8 --> E2f1
--------------- Total for Development targeting Prolifferation: 1 -------------
Pax8 --> Wt1
--------------- Total for Development targeting Posterior: 1 -------------
--------------- Total for Development targeting Immune response: 0 -------------
--------------- Total for Development targeting Development: 0 -------------
--------------- Total for Development targeting Forebrain: 0 -------------
--------------------------------------------------------------------------------
                       Forebrain	
2 Tfs: 	Tfap2c, Mybl2	                        
--------------------------------------------------------------------------------
--------------- Total for Forebrain targeting Prolifferation: 0 -------------
--------------- Total for Forebrain targeting Posterior: 0 -------------
--------------- Total for Forebrain targeting Immune response: 0 -------------
Tfap2c --> Cdkn1a, Ret
--------------- Total for Forebrain targeting Development: 2 -------------
--------------- Total for Forebrain targeting Forebrain: 0 -------------
E2f1 

Print out all combinations of subset analyese

In [42]:
def combinations(arr):
     
    # number of arrays
    n = len(arr)
    all_conds = []
    # to keep track of next element 
    # in each of the n arrays
    indices = [0 for i in range(n)]
 
    while (1):
 
        # prcurrent combination
        cond_1 = ''
        for i in range(n):
            cond_1 += arr[i][indices[i]] + ' '
        all_conds.append(cond_1)
 
        # find the rightmost array that has more
        # elements left after the current element
        # in that array
        next = n - 1
        while (next >= 0 and
              (indices[next] + 1 >= len(arr[next]))):
            next-=1
 
        # no such array is found so no more
        # combinations left
        if (next < 0):
            break
 
        # if found move to next element in that
        # array
        indices[next] += 1
 
        # for all arrays to the right of this
        # array current index again points to
        # first element
        for i in range(next + 1, n):
            indices[i] = 0

    return all_conds


import itertools

fb = ['fb-up', 'fb-no-change', 'fb-down']
mb = ['mb-up', 'mb-no-change', 'mb-down']
hb = ['hb-up', 'hb-no-change', 'hb-down']
sc = ['sc-up', 'sc-no-change', 'sc-down']
time = ['11>e18', '11=e18', '11<18']
marked_10 = ['marked-e10', 'not-marked-10']
marked_16 = ['marked-e16', 'not-marked-16']
conds = [fb, sc, time, marked_10]
print(len(combinations([fb, sc, marked_10])))
print(len(combinations([fb, sc, time, marked_10])))
print(len(combinations([fb, sc, marked_10, marked_16])))
print(len(combinations([fb, sc, time, marked_10, marked_16])))
print(len(combinations([fb, mb, hb, sc, marked_10, marked_16])))
print(len(combinations([fb, mb, hb, sc, time, marked_10, marked_16])))
18
54
36
108
324
972

Test VAE model against other models

Here we test other summary methods for gene separability.

In [132]:
import umap
import phate
from sklearn.manifold import TSNE

fb_genes = ['Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6', 'Arx', 'Dlx1', 'Dlx2', 'Dlx5', 'Nr2e2', 'Otx2']
mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4']
hb_genes = ['Hoxb1', 'Krox20', 'Fev', 'Hoxd3', 'Phox2b']
sc_genes = ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11', 'Hoxa13',
            'Hoxb9', 'Hoxb13',  'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13']

ns_glutamatergic = ['Slc17a6', 'VGLUT2', 'Slc17a7', 'VGLUT1', 'Slc17a8', 'VGLUT3', 'Slc1a1', 'Slc1a2', 'Slc1a6']
ns_progenitors = ['Sox2', 'Hes1', 'Hes5', 'Vim']
prolif = ['Ccna1', 'Ccna2', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Cdc25a', 
'Cdc25b', 'Cdc25c', 'E2f1', 'E2f2', 'E2f3', 'Mcm10', 'Mcm5', 'Mcm3', 'Mcm2', 'Cip2a']

lc = [c for c in df.columns if 'log2FoldChange' in c] 
logfc_values = df[lc + ['H3K27me3']].values
data_tsne = TSNE(n_components=3).fit_transform(vae_input_values)

phate_op = phate.PHATE(n_components=3)
data_phate = phate_op.fit_transform(vae_input_values)
umap_e = umap.UMAP(n_components=3).fit_transform(vae_input_values)
pca_vae = PCA(n_components=3)
pca_values = pca_vae.fit_transform(vae_input_values)
data_m = {
    'VAE-deep': vae_data,
    'VAE-lin': vae_data_shallow,
    'PCA': pca_values,
    'UMAP': umap_e,
    'PHATE': data_phate,
    'TSNE': data_tsne,
    'LogFC-H3K27me3': logfc_values,
    'RNA': rna_values,
    'Input': vae_input_values
}

        
gene_markers_sep = [prolif_genes, fb_genes, mb_genes, hb_genes, sc_genes]
marker_labels_sep = ['Proliferation', 'forebrain', 'midbrain', 'hindbrain',  'spinalcord']

color_map = {}
i = 6
for c in marker_labels_sep:
    if 'brain' in c or 'spinal' in c:
        if 'spinal' in c:
            color_map[c] = sc_colour
        else:
            color_map[c] = get_tissue_colour(c.lower())
    else:
        color_map[c] = sci_colour[i]
    i += 1
    
    

vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
                            gene_markers_sep, output_dir=fig_dir, plt_bg=False, vae_data=data_tsne, 
                                fig_type="svg",
                            title=f'Markers sep tsne {experiment_name}',
                            show_plt=False, save_fig=False, color_map=color_map, angle_plot=15)

vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
                            gene_markers_sep, output_dir=fig_dir, plt_bg=False,vae_data=vae_data, fig_type="svg",
                            title=f'Markers sep deep VAE {experiment_name}',
                            show_plt=False, save_fig=False, color_map=color_map, angle_plot=15)

vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
                            gene_markers_sep, output_dir=fig_dir, plt_bg=False,vae_data=pca_values, fig_type="svg",
                            title=f'Markers sep PCA {experiment_name}',
                            show_plt=False, save_fig=False, color_map=color_map, angle_plot=15)

vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
                            gene_markers_sep, output_dir=fig_dir, plt_bg=False,vae_data=umap_e, fig_type="svg",
                            title=f'Markers sep UMAP {experiment_name}',
                            show_plt=False, save_fig=False, color_map=color_map, angle_plot=15)

vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
                            gene_markers_sep, output_dir=fig_dir, plt_bg=False, vae_data=vae_data_shallow, fig_type="svg",
                            title=f'Markers sep shallow VAE {experiment_name}',
                            show_plt=False, save_fig=False, color_map=color_map, angle_plot=15)

vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
                            gene_markers_sep, output_dir=fig_dir, plt_bg=False, vae_data=data_phate, 
                                fig_type="svg",
                            title=f'Markers sep phate {experiment_name}',
                            show_plt=False, save_fig=False, color_map=color_map, angle_plot=15)

vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
                            gene_markers_sep, output_dir=fig_dir, plt_bg=False,vae_data=pca_values, fig_type="svg",
                            title=f'Markers sep PCA {experiment_name}',
                            show_plt=False, save_fig=False, color_map=color_map, angle_plot=15)

vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
                            gene_markers_sep, output_dir=fig_dir, plt_bg=False, vae_data=vae_data_shallow, fig_type="svg",
                            title=f'Markers sep shallow VAE {experiment_name}',
                            show_plt=False, save_fig=False, color_map=color_map, angle_plot=15)
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.20 seconds.
    Calculating affinities...
  Calculated graph and diffusion operator in 0.21 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 0.89 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.17 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 4.87 seconds.
Calculated PHATE in 6.15 seconds.
Out[132]:
<Axes3DSubplot:xlabel='Node 1', ylabel='Node 2'>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>
<Figure size 144x144 with 0 Axes>

Test as a distance metric

Some were done above but some remain

In [43]:
import random
import umap
import phate
from sklearn.manifold import TSNE

def get_shallow_vae(values, num_nodes):
    config = {'loss': {'loss_type': 'mse', 'distance_metric': 'mmd', 'mmd_weight': 1.0}, 
          'encoding': {'layers': []}, 
          'decoding': {'layers': []}, 
          'latent': {'num_nodes': num_nodes}, 'optimiser': {'params': {}, 'name': 'adam'}, 
          'seed': random.randint(0,100)} # ensure there is a random seed just like in UMAP and TSNE

    vae_shallow = VAE(values, values, labels, config, f'vae_{num_nodes}')
    vae_shallow.encode('default', epochs=100, batch_size=50)
    # Encode the same using the linear vae
    vae_data_shallow = vae_shallow.encode_new_data(values)
    return  vae_data_shallow, vae_shallow
    
    
def get_deep_vae(values, num_nodes):
    config = {'loss': {'loss_type': 'mse', 'distance_metric': 'mmd', 'mmd_weight': 1.0, 
                       'mmcd_method': 'k'}, 
                      'encoding': {'layers': [{'num_nodes': 64, 'activation_fn': 'selu'}, 
                                              {'num_nodes': 32, 'activation_fn': 'relu'}
                                              ]}, 
                      'decoding': {'layers': [
                                              {'num_nodes': 32, 'activation_fn': 'relu'},
                                              {'num_nodes': 64, 'activation_fn': 'selu'}]}, 
                      'latent': {'num_nodes': num_nodes}, 'optimiser': {'params': {}, 'name': 'adam'}, 
             'seed': random.randint(0,100)}

    vae_mse = VAE(values, values, labels, config, f'runs')
    vae_mse.encode('default', epochs=100, batch_size=50)
    vae_data = vae_mse.encode_new_data(values)
    return vae_data, vae_mse
In [44]:
sc_df = pd.read_csv('../../data/input/supps/GO/GO_SPINAL_CORD_DEVELOPMENT.csv')
mb_df = pd.read_csv('../../data/input/supps/GO/GO_MIDBRAIN_DEVELOPMENT.csv')
hb_df = pd.read_csv('../../data/input/supps/GO/GO_HINDBRAIN_DEVELOPMENT.csv')
fb_df = pd.read_csv('../../data/input/supps/GO/GO_FOREBRAIN_DEVELOPMENT.csv')

sc_go_genes = sc_df.values[:,0]
hb_go_genes = hb_df.values[:,0]
mb_go_genes = mb_df.values[:,0]
fb_go_genes = fb_df.values[:,0]

# Make sure there are no overlapping genes
sc_go = [g for g in sc_go_genes if g not in hb_go_genes and g not in mb_go_genes and g not in fb_go_genes]
hb_go = [g for g in hb_go_genes if g not in sc_go_genes and g not in mb_go_genes and g not in fb_go_genes]
mb_go = [g for g in mb_go_genes if g not in hb_go_genes and g not in sc_go_genes and g not in fb_go_genes]
fb_go = [g for g in fb_go_genes if g not in hb_go_genes and g not in mb_go_genes and g not in sc_go_genes]
In [47]:
results_indep = {}
results_tog = {}
results_t = {}

fb_genes = ['Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6', 'Arx', 'Dlx1', 'Dlx2', 'Dlx5', 'Nr2e2', 'Otx2']
mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4']
hb_genes = ['Hoxb1', 'Krox20', 'Fev', 'Hoxd3', 'Phox2b']
sc_genes = ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11', 'Hoxa13',
            'Hoxb9', 'Hoxb13',  'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13']

ns_glutamatergic = ['Slc17a6', 'VGLUT2', 'Slc17a7', 'VGLUT1', 'Slc17a8', 'VGLUT3', 'Slc1a1', 'Slc1a2', 'Slc1a6']
ns_progenitors = ['Sox2', 'Hes1', 'Hes5', 'Vim']
prolif = ['Ccna1', 'Ccna2', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Cdc25a', 
'Cdc25b', 'Cdc25c', 'E2f1', 'E2f2', 'E2f3', 'Mcm10', 'Mcm5', 'Mcm3', 'Mcm2', 'Cip2a']

comp = ['SC and FB', 
        'SC and MB',
        'SC and HB', 
        'SC and Pro.',
        'MB and FB', 
        'MB and HB',
        'MB and Pro.',
        'HB and FB',
        'HB and Pro.', 
        'FB and Pro.']

data_m = {
        'VAE-deep': None,
        'VAE-lin': None,
        'PCA': None,
        'UMAP': None,
        'PHATE': None,
        'TSNE': None
    }

for c in comp:
    results_indep[c] = {}
    results_tog[c] = {}
    results_t[c] = {}
    for label, data in data_m.items():
        results_indep[c][label] = 0
        results_tog[c][label] = 0
        results_t[c][label] = 0
        
t_tsts = 0
i_tsts = 0
# Keep track of the generated results 
data_ms = []
data_transforms = []

        
for tn in range(0, 30):
    ## Do a number of tests to compare how we'll each method is able to 
    ## reproduce the 
    phate_op = phate.PHATE(n_components=3, random_state=random.randint(0,100))
    phate_data = phate_op.fit_transform(vae_input_values)
    umap_op = umap.UMAP(n_components=3, 
                          random_state=random.randint(0,100), 
                          transform_seed=random.randint(0,100))
    umap_data = umap_op.fit_transform(vae_input_values)
    pca_op = PCA(n_components=3)
    
    pca_data = pca_op.fit_transform(vae_input_values)
    deep_vae_data, d_vae = get_deep_vae(vae_input_values, 3)
    shallow_vae_data, s_vae = get_shallow_vae(vae_input_values, 3)
    tsne_op = TSNE(n_components=3, random_state=random.randint(0,100))
    tsne_data = tsne_op.fit_transform(vae_input_values)

    data_m = {
        'VAE-deep': deep_vae_data,
        'VAE-lin': shallow_vae_data,
        'PCA': pca_data,
        'UMAP': umap_data,
        'PHATE': phate_data,
        'TSNE': tsne_data
    }
    data_trans = {
        'VAE-deep': d_vae,
        'VAE-lin': s_vae,
        'PCA': pca_op,
        'UMAP': umap_op,
        'PHATE': phate_op,
        'TSNE': tsne_op
    }
    # Add the data to our list
    data_ms.append(data_m)
    data_transforms.append(data_trans)
    t_tsts += 1
    i_tsts += 3
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.22 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.24 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.16 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.20 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 5.98 seconds.
Calculated PHATE in 7.58 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_6 (Dense)                 (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_7 (Dense)                 (None, 32)           2080        dense_6[0][0]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_7[0][0]                    
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_7[0][0]                    
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_8 (Dense)              (None, 32)                128       
_________________________________________________________________
dense_9 (Dense)              (None, 64)                2112      
_________________________________________________________________
dense_10 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_6 (Dense)                 (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_7 (Dense)                 (None, 32)           2080        dense_6[0][0]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_7[0][0]                    
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_7[0][0]                    
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_20 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_20 (T [()]                 0           tf_op_layer_Shape_20[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Pack_2 (TensorFlowO [(2,)]               0           tf_op_layer_strided_slice_20[0][0
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_2[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mul_6 (TensorFlowOp [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_2 (TensorFlowOp [(None, 3)]          0           tf_op_layer_Mul_6[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_21 (TensorFlo [(2,)]               0           tf_op_layer_Add_2[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_23 (TensorFlo [(2,)]               0           tf_op_layer_Add_2[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_22 (TensorFlo [(2,)]               0           tf_op_layer_Add_2[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_24 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_26 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_25 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_27 (TensorFlo [(2,)]               0           tf_op_layer_Add_2[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_29 (TensorFlo [(2,)]               0           tf_op_layer_Add_2[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_28 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_21 (T [()]                 0           tf_op_layer_Shape_21[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_23 (T [()]                 0           tf_op_layer_Shape_23[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_22 (T [()]                 0           tf_op_layer_Shape_22[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_24 (T [()]                 0           tf_op_layer_Shape_24[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_26 (T [()]                 0           tf_op_layer_Shape_26[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_25 (T [()]                 0           tf_op_layer_Shape_25[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_27 (T [()]                 0           tf_op_layer_Shape_27[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_29 (T [()]                 0           tf_op_layer_Shape_29[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_28 (T [()]                 0           tf_op_layer_Shape_28[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Reshape_12/shape (T [(3,)]               0           tf_op_layer_strided_slice_21[0][0
                                                                 tf_op_layer_strided_slice_23[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_13/shape (T [(3,)]               0           tf_op_layer_strided_slice_22[0][0
                                                                 tf_op_layer_strided_slice_23[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_14/shape (T [(3,)]               0           tf_op_layer_strided_slice_24[0][0
                                                                 tf_op_layer_strided_slice_26[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_15/shape (T [(3,)]               0           tf_op_layer_strided_slice_25[0][0
                                                                 tf_op_layer_strided_slice_26[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_16/shape (T [(3,)]               0           tf_op_layer_strided_slice_27[0][0
                                                                 tf_op_layer_strided_slice_29[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_17/shape (T [(3,)]               0           tf_op_layer_strided_slice_28[0][0
                                                                 tf_op_layer_strided_slice_29[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_12 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_2[0][0]          
                                                                 tf_op_layer_Reshape_12/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_12/multiples ( [(3,)]               0           tf_op_layer_strided_slice_22[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_13 (TensorF [(1, None, None)]    0           tf_op_layer_Add_2[0][0]          
                                                                 tf_op_layer_Reshape_13/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_13/multiples ( [(3,)]               0           tf_op_layer_strided_slice_21[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_14 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_14/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_14/multiples ( [(3,)]               0           tf_op_layer_strided_slice_25[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_15 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_15/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_15/multiples ( [(3,)]               0           tf_op_layer_strided_slice_24[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_16 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_2[0][0]          
                                                                 tf_op_layer_Reshape_16/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_16/multiples ( [(3,)]               0           tf_op_layer_strided_slice_28[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_17 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_17/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_17/multiples ( [(3,)]               0           tf_op_layer_strided_slice_27[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_12 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_12[0][0]     
                                                                 tf_op_layer_Tile_12/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_13 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_13[0][0]     
                                                                 tf_op_layer_Tile_13/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_14 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_14[0][0]     
                                                                 tf_op_layer_Tile_14/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_15 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_15[0][0]     
                                                                 tf_op_layer_Tile_15/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_16 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_16[0][0]     
                                                                 tf_op_layer_Tile_16/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_17 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_17[0][0]     
                                                                 tf_op_layer_Tile_17/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_10 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_12[0][0]        
                                                                 tf_op_layer_Tile_13[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_11 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_14[0][0]        
                                                                 tf_op_layer_Tile_15[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_12 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_16[0][0]        
                                                                 tf_op_layer_Tile_17[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_8 (TensorFlo [(None, None, None)] 0           tf_op_layer_Sub_10[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_9 (TensorFlo [(None, None, None)] 0           tf_op_layer_Sub_11[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_10 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_12[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_14 (TensorFlow [(None, None)]       0           tf_op_layer_Square_8[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mean_15 (TensorFlow [(None, None)]       0           tf_op_layer_Square_9[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mean_16 (TensorFlow [(None, None)]       0           tf_op_layer_Square_10[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_6 (TensorFlowOp [(None, None)]       0           tf_op_layer_Mean_14[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_6 (TensorFlowO [()]                 0           tf_op_layer_strided_slice_23[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_7 (TensorFlowOp [(None, None)]       0           tf_op_layer_Mean_15[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_7 (TensorFlowO [()]                 0           tf_op_layer_strided_slice_26[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_8 (TensorFlowOp [(None, None)]       0           tf_op_layer_Mean_16[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_8 (TensorFlowO [()]                 0           tf_op_layer_strided_slice_29[0][0
__________________________________________________________________________________________________
tf_op_layer_RealDiv_6 (TensorFl [(None, None)]       0           tf_op_layer_Neg_6[0][0]          
                                                                 tf_op_layer_Cast_6[0][0]         
__________________________________________________________________________________________________
tf_op_layer_RealDiv_7 (TensorFl [(None, None)]       0           tf_op_layer_Neg_7[0][0]          
                                                                 tf_op_layer_Cast_7[0][0]         
__________________________________________________________________________________________________
tf_op_layer_RealDiv_8 (TensorFl [(None, None)]       0           tf_op_layer_Neg_8[0][0]          
                                                                 tf_op_layer_Cast_8[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Exp_6 (TensorFlowOp [(None, None)]       0           tf_op_layer_RealDiv_6[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Exp_7 (TensorFlowOp [(None, None)]       0           tf_op_layer_RealDiv_7[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Exp_8 (TensorFlowOp [(None, None)]       0           tf_op_layer_RealDiv_8[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_17 (TensorFlow [()]                 0           tf_op_layer_Exp_6[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Mean_18 (TensorFlow [()]                 0           tf_op_layer_Exp_7[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Mean_19 (TensorFlow [()]                 0           tf_op_layer_Exp_8[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Sub_14 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_4 (TensorFlow [()]                 0           tf_op_layer_Mean_17[0][0]        
                                                                 tf_op_layer_Mean_18[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_7 (TensorFlowOp [()]                 0           tf_op_layer_Mean_19[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_11 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_14[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_13 (TensorFlowO [()]                 0           tf_op_layer_AddV2_4[0][0]        
                                                                 tf_op_layer_Mul_7[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Sum_2 (TensorFlowOp [(None,)]            0           tf_op_layer_Square_11[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_8 (TensorFlowOp [()]                 0           tf_op_layer_Sub_13[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_5 (TensorFlow [(None,)]            0           tf_op_layer_Sum_2[0][0]          
                                                                 tf_op_layer_Mul_8[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Mean_20 (TensorFlow [()]                 0           tf_op_layer_AddV2_5[0][0]        
__________________________________________________________________________________________________
add_loss_2 (AddLoss)            ()                   0           tf_op_layer_Mean_20[0][0]        
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.8348 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1614s). Check your callbacks.
24/24 [==============================] - 1s 26ms/step - loss: 4.2879 - val_loss: 1.8808
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5959 - val_loss: 1.2249
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2933 - val_loss: 1.1093
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2367 - val_loss: 1.0628
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1880 - val_loss: 1.0370
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1586 - val_loss: 1.0237
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1758 - val_loss: 1.0361
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1346 - val_loss: 1.0146
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0566 - val_loss: 0.9109
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9575 - val_loss: 0.8620
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8908 - val_loss: 0.8349
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8749 - val_loss: 0.7807
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8568 - val_loss: 0.7706
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8310 - val_loss: 0.7576
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8298 - val_loss: 0.7670
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8199 - val_loss: 0.7768
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8223 - val_loss: 0.7584
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8082 - val_loss: 0.7482
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8039 - val_loss: 0.7387
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8035 - val_loss: 0.7471
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8069 - val_loss: 0.7314
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7998 - val_loss: 0.7326
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7933 - val_loss: 0.7291
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7850 - val_loss: 0.7211
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7848 - val_loss: 0.7210
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7825 - val_loss: 0.7128
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7664 - val_loss: 0.7053
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7708 - val_loss: 0.7113
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7783 - val_loss: 0.6968
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7713 - val_loss: 0.7250
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7607 - val_loss: 0.7224
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7799 - val_loss: 0.7109
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7687 - val_loss: 0.6982
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7516 - val_loss: 0.7123
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7606 - val_loss: 0.7135
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7507 - val_loss: 0.7079
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7556 - val_loss: 0.6868
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7578 - val_loss: 0.7282
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7553 - val_loss: 0.7118
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7450 - val_loss: 0.7244
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7384 - val_loss: 0.7043
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7502 - val_loss: 0.7010
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7318 - val_loss: 0.6871
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7402 - val_loss: 0.6972
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7300 - val_loss: 0.6976
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7433 - val_loss: 0.6767
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7339 - val_loss: 0.6758
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7316 - val_loss: 0.6950
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7283 - val_loss: 0.6908
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7346 - val_loss: 0.6783
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7478 - val_loss: 0.6791
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7368 - val_loss: 0.6711
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7244 - val_loss: 0.6808
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7300 - val_loss: 0.7058
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7302 - val_loss: 0.6692
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7294 - val_loss: 0.6864
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7157 - val_loss: 0.6870
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7187 - val_loss: 0.6802
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7143 - val_loss: 0.6694
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7073 - val_loss: 0.7070
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7253 - val_loss: 0.6620
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7059 - val_loss: 0.6906
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7226 - val_loss: 0.6637
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7087 - val_loss: 0.6819
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7148 - val_loss: 0.6744
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7154 - val_loss: 0.6718
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7163 - val_loss: 0.6800
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7019 - val_loss: 0.6673
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7162 - val_loss: 0.6728
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7110 - val_loss: 0.6839
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7003 - val_loss: 0.6731
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7121 - val_loss: 0.6596
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7051 - val_loss: 0.6878
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7043 - val_loss: 0.6621
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7024 - val_loss: 0.6588
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6995 - val_loss: 0.6645
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6979 - val_loss: 0.6501
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7062 - val_loss: 0.6680
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7040 - val_loss: 0.6856
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7030 - val_loss: 0.6615
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7075 - val_loss: 0.6686
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6967 - val_loss: 0.6707
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7020 - val_loss: 0.6585
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6865 - val_loss: 0.6508
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6892 - val_loss: 0.6710
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6857 - val_loss: 0.6511
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7036 - val_loss: 0.6435
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6906 - val_loss: 0.7037
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6941 - val_loss: 0.6511
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6851 - val_loss: 0.6493
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6968 - val_loss: 0.6456
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6928 - val_loss: 0.6711
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6945 - val_loss: 0.6480
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6935 - val_loss: 0.6630
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6892 - val_loss: 0.6652
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6836 - val_loss: 0.6748
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6899 - val_loss: 0.6587
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6814 - val_loss: 0.6837
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6858 - val_loss: 0.6597
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6905 - val_loss: 0.6478
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_11 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_30 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_30 (T [()]                 0           tf_op_layer_Shape_30[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Pack_3 (TensorFlowO [(2,)]               0           tf_op_layer_strided_slice_30[0][0
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_3[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mul_9 (TensorFlowOp [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_3 (TensorFlowOp [(None, 3)]          0           tf_op_layer_Mul_9[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_31 (TensorFlo [(2,)]               0           tf_op_layer_Add_3[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_33 (TensorFlo [(2,)]               0           tf_op_layer_Add_3[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_32 (TensorFlo [(2,)]               0           tf_op_layer_Add_3[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_34 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_36 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_35 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_37 (TensorFlo [(2,)]               0           tf_op_layer_Add_3[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_39 (TensorFlo [(2,)]               0           tf_op_layer_Add_3[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_38 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_31 (T [()]                 0           tf_op_layer_Shape_31[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_33 (T [()]                 0           tf_op_layer_Shape_33[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_32 (T [()]                 0           tf_op_layer_Shape_32[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_34 (T [()]                 0           tf_op_layer_Shape_34[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_36 (T [()]                 0           tf_op_layer_Shape_36[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_35 (T [()]                 0           tf_op_layer_Shape_35[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_37 (T [()]                 0           tf_op_layer_Shape_37[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_39 (T [()]                 0           tf_op_layer_Shape_39[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_38 (T [()]                 0           tf_op_layer_Shape_38[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Reshape_18/shape (T [(3,)]               0           tf_op_layer_strided_slice_31[0][0
                                                                 tf_op_layer_strided_slice_33[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_19/shape (T [(3,)]               0           tf_op_layer_strided_slice_32[0][0
                                                                 tf_op_layer_strided_slice_33[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_20/shape (T [(3,)]               0           tf_op_layer_strided_slice_34[0][0
                                                                 tf_op_layer_strided_slice_36[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_21/shape (T [(3,)]               0           tf_op_layer_strided_slice_35[0][0
                                                                 tf_op_layer_strided_slice_36[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_22/shape (T [(3,)]               0           tf_op_layer_strided_slice_37[0][0
                                                                 tf_op_layer_strided_slice_39[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_23/shape (T [(3,)]               0           tf_op_layer_strided_slice_38[0][0
                                                                 tf_op_layer_strided_slice_39[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_18 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_3[0][0]          
                                                                 tf_op_layer_Reshape_18/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_18/multiples ( [(3,)]               0           tf_op_layer_strided_slice_32[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_19 (TensorF [(1, None, None)]    0           tf_op_layer_Add_3[0][0]          
                                                                 tf_op_layer_Reshape_19/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_19/multiples ( [(3,)]               0           tf_op_layer_strided_slice_31[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_20 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_20/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_20/multiples ( [(3,)]               0           tf_op_layer_strided_slice_35[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_21 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_21/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_21/multiples ( [(3,)]               0           tf_op_layer_strided_slice_34[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_22 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_3[0][0]          
                                                                 tf_op_layer_Reshape_22/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_22/multiples ( [(3,)]               0           tf_op_layer_strided_slice_38[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_23 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_23/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_23/multiples ( [(3,)]               0           tf_op_layer_strided_slice_37[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_18 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_18[0][0]     
                                                                 tf_op_layer_Tile_18/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_19 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_19[0][0]     
                                                                 tf_op_layer_Tile_19/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_20 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_20[0][0]     
                                                                 tf_op_layer_Tile_20/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_21 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_21[0][0]     
                                                                 tf_op_layer_Tile_21/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_22 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_22[0][0]     
                                                                 tf_op_layer_Tile_22/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_23 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_23[0][0]     
                                                                 tf_op_layer_Tile_23/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_15 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_18[0][0]        
                                                                 tf_op_layer_Tile_19[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_16 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_20[0][0]        
                                                                 tf_op_layer_Tile_21[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_17 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_22[0][0]        
                                                                 tf_op_layer_Tile_23[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_12 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_15[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_13 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_16[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_14 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_17[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_21 (TensorFlow [(None, None)]       0           tf_op_layer_Square_12[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_22 (TensorFlow [(None, None)]       0           tf_op_layer_Square_13[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_23 (TensorFlow [(None, None)]       0           tf_op_layer_Square_14[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_9 (TensorFlowOp [(None, None)]       0           tf_op_layer_Mean_21[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_9 (TensorFlowO [()]                 0           tf_op_layer_strided_slice_33[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_10 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_22[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_10 (TensorFlow [()]                 0           tf_op_layer_strided_slice_36[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_11 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_23[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_11 (TensorFlow [()]                 0           tf_op_layer_strided_slice_39[0][0
__________________________________________________________________________________________________
tf_op_layer_RealDiv_9 (TensorFl [(None, None)]       0           tf_op_layer_Neg_9[0][0]          
                                                                 tf_op_layer_Cast_9[0][0]         
__________________________________________________________________________________________________
tf_op_layer_RealDiv_10 (TensorF [(None, None)]       0           tf_op_layer_Neg_10[0][0]         
                                                                 tf_op_layer_Cast_10[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_11 (TensorF [(None, None)]       0           tf_op_layer_Neg_11[0][0]         
                                                                 tf_op_layer_Cast_11[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_9 (TensorFlowOp [(None, None)]       0           tf_op_layer_RealDiv_9[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Exp_10 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_10[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_11 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_11[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_24 (TensorFlow [()]                 0           tf_op_layer_Exp_9[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Mean_25 (TensorFlow [()]                 0           tf_op_layer_Exp_10[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_26 (TensorFlow [()]                 0           tf_op_layer_Exp_11[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_19 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_6 (TensorFlow [()]                 0           tf_op_layer_Mean_24[0][0]        
                                                                 tf_op_layer_Mean_25[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_10 (TensorFlowO [()]                 0           tf_op_layer_Mean_26[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_15 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_19[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_18 (TensorFlowO [()]                 0           tf_op_layer_AddV2_6[0][0]        
                                                                 tf_op_layer_Mul_10[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_3 (TensorFlowOp [(None,)]            0           tf_op_layer_Square_15[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_11 (TensorFlowO [()]                 0           tf_op_layer_Sub_18[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_7 (TensorFlow [(None,)]            0           tf_op_layer_Sum_3[0][0]          
                                                                 tf_op_layer_Mul_11[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_27 (TensorFlow [()]                 0           tf_op_layer_AddV2_7[0][0]        
__________________________________________________________________________________________________
add_loss_3 (AddLoss)            ()                   0           tf_op_layer_Mean_27[0][0]        
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.7807WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.1106s). Check your callbacks.
24/24 [==============================] - 0s 17ms/step - loss: 8.3184 - val_loss: 5.7205
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.7519 - val_loss: 3.5428
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.9736 - val_loss: 2.3224
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.1030 - val_loss: 1.8377
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8089 - val_loss: 1.6737
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6881 - val_loss: 1.5723
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5747 - val_loss: 1.4645
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4689 - val_loss: 1.4036
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3878 - val_loss: 1.3741
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3193 - val_loss: 1.2664
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2739 - val_loss: 1.2329
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2419 - val_loss: 1.2078
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1948 - val_loss: 1.1719
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1824 - val_loss: 1.1750
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1750 - val_loss: 1.1306
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1479 - val_loss: 1.1107
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1519 - val_loss: 1.1460
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1325 - val_loss: 1.0984
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1391 - val_loss: 1.1094
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1268 - val_loss: 1.1250
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1090 - val_loss: 1.1077
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1027 - val_loss: 1.1436
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0991 - val_loss: 1.0683
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0860 - val_loss: 1.0794
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0859 - val_loss: 1.0696
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0746 - val_loss: 1.1496
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0738 - val_loss: 1.0913
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0808 - val_loss: 1.0760
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0738 - val_loss: 1.0770
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0621 - val_loss: 1.0969
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0645 - val_loss: 1.0841
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0523 - val_loss: 1.0374
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0565 - val_loss: 1.0685
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0528 - val_loss: 1.0689
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0590 - val_loss: 1.0475
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0481 - val_loss: 1.0632
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0657 - val_loss: 1.0404
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0436 - val_loss: 1.0735
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0378 - val_loss: 1.0238
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0431 - val_loss: 1.0558
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0498 - val_loss: 1.0516
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0349 - val_loss: 1.1045
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0197 - val_loss: 1.0493
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0564 - val_loss: 1.0452
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0329 - val_loss: 1.0379
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0213 - val_loss: 1.0663
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0245 - val_loss: 1.0634
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0270 - val_loss: 1.0229
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0232 - val_loss: 1.0545
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0200 - val_loss: 1.0264
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0283 - val_loss: 1.0092
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0233 - val_loss: 1.0136
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0313 - val_loss: 1.0320
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0204 - val_loss: 1.0450
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0166 - val_loss: 1.0057
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0258 - val_loss: 1.0556
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0248 - val_loss: 1.0044
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0251 - val_loss: 1.0063
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0101 - val_loss: 1.0151
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0102 - val_loss: 1.0420
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0218 - val_loss: 1.0046
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9989 - val_loss: 1.0225
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0175 - val_loss: 1.0123
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0159 - val_loss: 1.0409
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0160 - val_loss: 0.9939
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0065 - val_loss: 1.0371
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0032 - val_loss: 0.9979
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0103 - val_loss: 0.9908
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9982 - val_loss: 1.0284
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9943 - val_loss: 0.9741
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9865 - val_loss: 1.0203
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0004 - val_loss: 1.0479
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0088 - val_loss: 1.0399
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9935 - val_loss: 0.9797
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9906 - val_loss: 0.9902
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9957 - val_loss: 1.0242
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0008 - val_loss: 0.9940
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9998 - val_loss: 1.0407
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0065 - val_loss: 0.9775
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0086 - val_loss: 0.9788
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9929 - val_loss: 0.9935
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0016 - val_loss: 0.9795
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9812 - val_loss: 1.0130
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9833 - val_loss: 0.9861
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9800 - val_loss: 1.0176
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9973 - val_loss: 0.9965
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9897 - val_loss: 0.9888
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9739 - val_loss: 0.9873
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9884 - val_loss: 0.9848
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9949 - val_loss: 0.9828
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9783 - val_loss: 1.0121
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9969 - val_loss: 0.9786
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9903 - val_loss: 0.9846
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9825 - val_loss: 0.9868
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9898 - val_loss: 0.9828
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9710 - val_loss: 0.9988
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9881 - val_loss: 0.9839
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9843 - val_loss: 1.0174
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9724 - val_loss: 0.9708
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9824 - val_loss: 0.9721
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.29 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.31 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.54 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.24 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 8.29 seconds.
Calculated PHATE in 10.40 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_12 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_13 (Dense)                (None, 32)           2080        dense_12[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_13[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_13[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_14 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_15 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_16 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_12 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_13 (Dense)                (None, 32)           2080        dense_12[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_13[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_13[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_40 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_40 (T [()]                 0           tf_op_layer_Shape_40[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Pack_4 (TensorFlowO [(2,)]               0           tf_op_layer_strided_slice_40[0][0
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_4[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mul_12 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_4 (TensorFlowOp [(None, 3)]          0           tf_op_layer_Mul_12[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_41 (TensorFlo [(2,)]               0           tf_op_layer_Add_4[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_43 (TensorFlo [(2,)]               0           tf_op_layer_Add_4[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_42 (TensorFlo [(2,)]               0           tf_op_layer_Add_4[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_44 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_46 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_45 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_47 (TensorFlo [(2,)]               0           tf_op_layer_Add_4[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_49 (TensorFlo [(2,)]               0           tf_op_layer_Add_4[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_48 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_41 (T [()]                 0           tf_op_layer_Shape_41[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_43 (T [()]                 0           tf_op_layer_Shape_43[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_42 (T [()]                 0           tf_op_layer_Shape_42[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_44 (T [()]                 0           tf_op_layer_Shape_44[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_46 (T [()]                 0           tf_op_layer_Shape_46[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_45 (T [()]                 0           tf_op_layer_Shape_45[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_47 (T [()]                 0           tf_op_layer_Shape_47[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_49 (T [()]                 0           tf_op_layer_Shape_49[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_48 (T [()]                 0           tf_op_layer_Shape_48[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Reshape_24/shape (T [(3,)]               0           tf_op_layer_strided_slice_41[0][0
                                                                 tf_op_layer_strided_slice_43[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_25/shape (T [(3,)]               0           tf_op_layer_strided_slice_42[0][0
                                                                 tf_op_layer_strided_slice_43[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_26/shape (T [(3,)]               0           tf_op_layer_strided_slice_44[0][0
                                                                 tf_op_layer_strided_slice_46[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_27/shape (T [(3,)]               0           tf_op_layer_strided_slice_45[0][0
                                                                 tf_op_layer_strided_slice_46[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_28/shape (T [(3,)]               0           tf_op_layer_strided_slice_47[0][0
                                                                 tf_op_layer_strided_slice_49[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_29/shape (T [(3,)]               0           tf_op_layer_strided_slice_48[0][0
                                                                 tf_op_layer_strided_slice_49[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_24 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_4[0][0]          
                                                                 tf_op_layer_Reshape_24/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_24/multiples ( [(3,)]               0           tf_op_layer_strided_slice_42[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_25 (TensorF [(1, None, None)]    0           tf_op_layer_Add_4[0][0]          
                                                                 tf_op_layer_Reshape_25/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_25/multiples ( [(3,)]               0           tf_op_layer_strided_slice_41[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_26 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_26/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_26/multiples ( [(3,)]               0           tf_op_layer_strided_slice_45[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_27 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_27/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_27/multiples ( [(3,)]               0           tf_op_layer_strided_slice_44[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_28 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_4[0][0]          
                                                                 tf_op_layer_Reshape_28/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_28/multiples ( [(3,)]               0           tf_op_layer_strided_slice_48[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_29 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_29/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_29/multiples ( [(3,)]               0           tf_op_layer_strided_slice_47[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_24 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_24[0][0]     
                                                                 tf_op_layer_Tile_24/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_25 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_25[0][0]     
                                                                 tf_op_layer_Tile_25/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_26 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_26[0][0]     
                                                                 tf_op_layer_Tile_26/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_27 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_27[0][0]     
                                                                 tf_op_layer_Tile_27/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_28 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_28[0][0]     
                                                                 tf_op_layer_Tile_28/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_29 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_29[0][0]     
                                                                 tf_op_layer_Tile_29/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_20 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_24[0][0]        
                                                                 tf_op_layer_Tile_25[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_21 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_26[0][0]        
                                                                 tf_op_layer_Tile_27[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_22 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_28[0][0]        
                                                                 tf_op_layer_Tile_29[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_16 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_20[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_17 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_21[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_18 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_22[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_28 (TensorFlow [(None, None)]       0           tf_op_layer_Square_16[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_29 (TensorFlow [(None, None)]       0           tf_op_layer_Square_17[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_30 (TensorFlow [(None, None)]       0           tf_op_layer_Square_18[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_12 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_28[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_12 (TensorFlow [()]                 0           tf_op_layer_strided_slice_43[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_13 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_29[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_13 (TensorFlow [()]                 0           tf_op_layer_strided_slice_46[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_14 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_30[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_14 (TensorFlow [()]                 0           tf_op_layer_strided_slice_49[0][0
__________________________________________________________________________________________________
tf_op_layer_RealDiv_12 (TensorF [(None, None)]       0           tf_op_layer_Neg_12[0][0]         
                                                                 tf_op_layer_Cast_12[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_13 (TensorF [(None, None)]       0           tf_op_layer_Neg_13[0][0]         
                                                                 tf_op_layer_Cast_13[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_14 (TensorF [(None, None)]       0           tf_op_layer_Neg_14[0][0]         
                                                                 tf_op_layer_Cast_14[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_12 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_12[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_13 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_13[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_14 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_14[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_31 (TensorFlow [()]                 0           tf_op_layer_Exp_12[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_32 (TensorFlow [()]                 0           tf_op_layer_Exp_13[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_33 (TensorFlow [()]                 0           tf_op_layer_Exp_14[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_24 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_8 (TensorFlow [()]                 0           tf_op_layer_Mean_31[0][0]        
                                                                 tf_op_layer_Mean_32[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_13 (TensorFlowO [()]                 0           tf_op_layer_Mean_33[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_19 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_24[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_23 (TensorFlowO [()]                 0           tf_op_layer_AddV2_8[0][0]        
                                                                 tf_op_layer_Mul_13[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_4 (TensorFlowOp [(None,)]            0           tf_op_layer_Square_19[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_14 (TensorFlowO [()]                 0           tf_op_layer_Sub_23[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_9 (TensorFlow [(None,)]            0           tf_op_layer_Sum_4[0][0]          
                                                                 tf_op_layer_Mul_14[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_34 (TensorFlow [()]                 0           tf_op_layer_AddV2_9[0][0]        
__________________________________________________________________________________________________
add_loss_4 (AddLoss)            ()                   0           tf_op_layer_Mean_34[0][0]        
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.1797WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.1748s). Check your callbacks.
24/24 [==============================] - 1s 26ms/step - loss: 4.5324 - val_loss: 1.9202
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6054 - val_loss: 1.2037
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2470 - val_loss: 1.1075
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0950 - val_loss: 0.9366
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9390 - val_loss: 0.9156
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9058 - val_loss: 0.8415
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8633 - val_loss: 0.8088
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8588 - val_loss: 0.8371
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8397 - val_loss: 0.8065
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8424 - val_loss: 0.8511
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8258 - val_loss: 0.8117
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8164 - val_loss: 0.7998
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8101 - val_loss: 0.8284
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8003 - val_loss: 0.7879
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7880 - val_loss: 0.7645
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7916 - val_loss: 0.7933
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8049 - val_loss: 0.7959
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7895 - val_loss: 0.7704
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7769 - val_loss: 0.7663
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7665 - val_loss: 0.7600
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7606 - val_loss: 0.7916
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7583 - val_loss: 0.7837
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7603 - val_loss: 0.8015
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7532 - val_loss: 0.7626
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7602 - val_loss: 0.7876
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7482 - val_loss: 0.8277
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7504 - val_loss: 0.7593
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7391 - val_loss: 0.7698
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7465 - val_loss: 0.7366
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7327 - val_loss: 0.7420
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7328 - val_loss: 0.7543
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7214 - val_loss: 0.7447
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7186 - val_loss: 0.7565
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7158 - val_loss: 0.7432
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7202 - val_loss: 0.7563
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7179 - val_loss: 0.7266
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7141 - val_loss: 0.7543
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7168 - val_loss: 0.7416
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7165 - val_loss: 0.7583
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6988 - val_loss: 0.7274
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7035 - val_loss: 0.7197
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7047 - val_loss: 0.7080
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7031 - val_loss: 0.7224
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7080 - val_loss: 0.7190
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7027 - val_loss: 0.7018
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6996 - val_loss: 0.7172
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6947 - val_loss: 0.7616
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7020 - val_loss: 0.7431
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6904 - val_loss: 0.7339
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6920 - val_loss: 0.7303
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6965 - val_loss: 0.7232
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6871 - val_loss: 0.7197
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6862 - val_loss: 0.7101
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6970 - val_loss: 0.7119
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6860 - val_loss: 0.7319
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6909 - val_loss: 0.7084
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6951 - val_loss: 0.7273
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6788 - val_loss: 0.7154
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6797 - val_loss: 0.7195
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6917 - val_loss: 0.7695
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6863 - val_loss: 0.7038
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6824 - val_loss: 0.6977
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6797 - val_loss: 0.7034
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6827 - val_loss: 0.7123
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6781 - val_loss: 0.7109
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6781 - val_loss: 0.7214
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6882 - val_loss: 0.7266
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6760 - val_loss: 0.7023
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7010 - val_loss: 0.7294
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6774 - val_loss: 0.7044
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6688 - val_loss: 0.7102
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6693 - val_loss: 0.6885
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6762 - val_loss: 0.7305
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6803 - val_loss: 0.7219
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6785 - val_loss: 0.6830
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6689 - val_loss: 0.6865
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6649 - val_loss: 0.7117
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6669 - val_loss: 0.7354
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6828 - val_loss: 0.7283
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6721 - val_loss: 0.6967
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6764 - val_loss: 0.6943
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6759 - val_loss: 0.6991
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6731 - val_loss: 0.7143
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6707 - val_loss: 0.6911
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6649 - val_loss: 0.6887
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6697 - val_loss: 0.6821
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6634 - val_loss: 0.6996
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6829 - val_loss: 0.7298
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6677 - val_loss: 0.7221
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6648 - val_loss: 0.7188
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6686 - val_loss: 0.6881
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6769 - val_loss: 0.7028
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6695 - val_loss: 0.6857
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6639 - val_loss: 0.7091
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6648 - val_loss: 0.6975
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6737 - val_loss: 0.6778
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6702 - val_loss: 0.7130
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6649 - val_loss: 0.6752
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6665 - val_loss: 0.6785
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6630 - val_loss: 0.7073
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_17 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_50 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_50 (T [()]                 0           tf_op_layer_Shape_50[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Pack_5 (TensorFlowO [(2,)]               0           tf_op_layer_strided_slice_50[0][0
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_5[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mul_15 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_5 (TensorFlowOp [(None, 3)]          0           tf_op_layer_Mul_15[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_51 (TensorFlo [(2,)]               0           tf_op_layer_Add_5[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_53 (TensorFlo [(2,)]               0           tf_op_layer_Add_5[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_52 (TensorFlo [(2,)]               0           tf_op_layer_Add_5[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_54 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_56 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_55 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_57 (TensorFlo [(2,)]               0           tf_op_layer_Add_5[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_59 (TensorFlo [(2,)]               0           tf_op_layer_Add_5[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_58 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_51 (T [()]                 0           tf_op_layer_Shape_51[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_53 (T [()]                 0           tf_op_layer_Shape_53[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_52 (T [()]                 0           tf_op_layer_Shape_52[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_54 (T [()]                 0           tf_op_layer_Shape_54[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_56 (T [()]                 0           tf_op_layer_Shape_56[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_55 (T [()]                 0           tf_op_layer_Shape_55[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_57 (T [()]                 0           tf_op_layer_Shape_57[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_59 (T [()]                 0           tf_op_layer_Shape_59[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_58 (T [()]                 0           tf_op_layer_Shape_58[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Reshape_30/shape (T [(3,)]               0           tf_op_layer_strided_slice_51[0][0
                                                                 tf_op_layer_strided_slice_53[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_31/shape (T [(3,)]               0           tf_op_layer_strided_slice_52[0][0
                                                                 tf_op_layer_strided_slice_53[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_32/shape (T [(3,)]               0           tf_op_layer_strided_slice_54[0][0
                                                                 tf_op_layer_strided_slice_56[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_33/shape (T [(3,)]               0           tf_op_layer_strided_slice_55[0][0
                                                                 tf_op_layer_strided_slice_56[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_34/shape (T [(3,)]               0           tf_op_layer_strided_slice_57[0][0
                                                                 tf_op_layer_strided_slice_59[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_35/shape (T [(3,)]               0           tf_op_layer_strided_slice_58[0][0
                                                                 tf_op_layer_strided_slice_59[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_30 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_5[0][0]          
                                                                 tf_op_layer_Reshape_30/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_30/multiples ( [(3,)]               0           tf_op_layer_strided_slice_52[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_31 (TensorF [(1, None, None)]    0           tf_op_layer_Add_5[0][0]          
                                                                 tf_op_layer_Reshape_31/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_31/multiples ( [(3,)]               0           tf_op_layer_strided_slice_51[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_32 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_32/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_32/multiples ( [(3,)]               0           tf_op_layer_strided_slice_55[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_33 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_33/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_33/multiples ( [(3,)]               0           tf_op_layer_strided_slice_54[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_34 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_5[0][0]          
                                                                 tf_op_layer_Reshape_34/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_34/multiples ( [(3,)]               0           tf_op_layer_strided_slice_58[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_35 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_35/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_35/multiples ( [(3,)]               0           tf_op_layer_strided_slice_57[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_30 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_30[0][0]     
                                                                 tf_op_layer_Tile_30/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_31 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_31[0][0]     
                                                                 tf_op_layer_Tile_31/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_32 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_32[0][0]     
                                                                 tf_op_layer_Tile_32/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_33 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_33[0][0]     
                                                                 tf_op_layer_Tile_33/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_34 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_34[0][0]     
                                                                 tf_op_layer_Tile_34/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_35 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_35[0][0]     
                                                                 tf_op_layer_Tile_35/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_25 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_30[0][0]        
                                                                 tf_op_layer_Tile_31[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_26 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_32[0][0]        
                                                                 tf_op_layer_Tile_33[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_27 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_34[0][0]        
                                                                 tf_op_layer_Tile_35[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_20 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_25[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_21 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_26[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_22 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_27[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_35 (TensorFlow [(None, None)]       0           tf_op_layer_Square_20[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_36 (TensorFlow [(None, None)]       0           tf_op_layer_Square_21[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_37 (TensorFlow [(None, None)]       0           tf_op_layer_Square_22[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_15 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_35[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_15 (TensorFlow [()]                 0           tf_op_layer_strided_slice_53[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_16 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_36[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_16 (TensorFlow [()]                 0           tf_op_layer_strided_slice_56[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_17 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_37[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_17 (TensorFlow [()]                 0           tf_op_layer_strided_slice_59[0][0
__________________________________________________________________________________________________
tf_op_layer_RealDiv_15 (TensorF [(None, None)]       0           tf_op_layer_Neg_15[0][0]         
                                                                 tf_op_layer_Cast_15[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_16 (TensorF [(None, None)]       0           tf_op_layer_Neg_16[0][0]         
                                                                 tf_op_layer_Cast_16[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_17 (TensorF [(None, None)]       0           tf_op_layer_Neg_17[0][0]         
                                                                 tf_op_layer_Cast_17[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_15 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_15[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_16 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_16[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_17 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_17[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_38 (TensorFlow [()]                 0           tf_op_layer_Exp_15[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_39 (TensorFlow [()]                 0           tf_op_layer_Exp_16[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_40 (TensorFlow [()]                 0           tf_op_layer_Exp_17[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_29 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_10 (TensorFlo [()]                 0           tf_op_layer_Mean_38[0][0]        
                                                                 tf_op_layer_Mean_39[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_16 (TensorFlowO [()]                 0           tf_op_layer_Mean_40[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_23 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_29[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_28 (TensorFlowO [()]                 0           tf_op_layer_AddV2_10[0][0]       
                                                                 tf_op_layer_Mul_16[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_5 (TensorFlowOp [(None,)]            0           tf_op_layer_Square_23[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_17 (TensorFlowO [()]                 0           tf_op_layer_Sub_28[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_11 (TensorFlo [(None,)]            0           tf_op_layer_Sum_5[0][0]          
                                                                 tf_op_layer_Mul_17[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_41 (TensorFlow [()]                 0           tf_op_layer_AddV2_11[0][0]       
__________________________________________________________________________________________________
add_loss_5 (AddLoss)            ()                   0           tf_op_layer_Mean_41[0][0]        
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.8418WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1484s). Check your callbacks.
24/24 [==============================] - 0s 18ms/step - loss: 7.4517 - val_loss: 4.9728
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 3.6674 - val_loss: 2.6996
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.3367 - val_loss: 1.9405
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.9739 - val_loss: 1.7099
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7817 - val_loss: 1.6166
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6370 - val_loss: 1.4857
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5493 - val_loss: 1.4044
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4577 - val_loss: 1.3334
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3839 - val_loss: 1.2980
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3374 - val_loss: 1.2102
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2614 - val_loss: 1.1493
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2485 - val_loss: 1.1345
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2135 - val_loss: 1.1647
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2071 - val_loss: 1.0815
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2043 - val_loss: 1.1200
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1682 - val_loss: 1.0888
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1426 - val_loss: 1.0584
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1386 - val_loss: 1.0872
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1361 - val_loss: 1.0895
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1333 - val_loss: 1.0726
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1358 - val_loss: 1.0850
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1325 - val_loss: 1.0395
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1177 - val_loss: 1.0314
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0996 - val_loss: 1.0179
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0990 - val_loss: 1.0443
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0948 - val_loss: 1.0484
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0879 - val_loss: 1.0098
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0949 - val_loss: 1.0462
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0767 - val_loss: 1.0282
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0786 - val_loss: 1.0317
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0701 - val_loss: 1.0216
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0681 - val_loss: 1.0366
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0774 - val_loss: 0.9997
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0850 - val_loss: 1.0148
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0653 - val_loss: 1.0101
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0586 - val_loss: 1.0001
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0548 - val_loss: 1.0002
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0488 - val_loss: 0.9783
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0439 - val_loss: 1.0072
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0537 - val_loss: 0.9460
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0426 - val_loss: 0.9704
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0478 - val_loss: 0.9987
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0526 - val_loss: 0.9716
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0560 - val_loss: 0.9644
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0419 - val_loss: 0.9855
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0268 - val_loss: 0.9761
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0380 - val_loss: 0.9655
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0257 - val_loss: 0.9496
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0291 - val_loss: 0.9444
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0282 - val_loss: 0.9726
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0295 - val_loss: 0.9871
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0226 - val_loss: 0.9828
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0346 - val_loss: 0.9555
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0148 - val_loss: 0.9791
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0096 - val_loss: 0.9784
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0177 - val_loss: 0.9668
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0362 - val_loss: 0.9549
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0276 - val_loss: 0.9486
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0206 - val_loss: 0.9712
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0163 - val_loss: 0.9562
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0146 - val_loss: 0.9850
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0178 - val_loss: 0.9539
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0219 - val_loss: 0.9456
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0181 - val_loss: 0.9513
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0044 - val_loss: 0.9542
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0191 - val_loss: 0.9172
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0056 - val_loss: 0.9544
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9979 - val_loss: 0.9363
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0163 - val_loss: 0.9461
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0268 - val_loss: 0.9677
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0072 - val_loss: 0.9571
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0129 - val_loss: 0.9182
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9981 - val_loss: 0.9794
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0011 - val_loss: 0.9826
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0020 - val_loss: 0.9416
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9983 - val_loss: 0.9478
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0018 - val_loss: 0.9108
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9960 - val_loss: 0.9512
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9895 - val_loss: 0.9454
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9999 - val_loss: 0.9371
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0043 - val_loss: 0.9193
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9992 - val_loss: 0.9491
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9976 - val_loss: 0.9286
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9986 - val_loss: 0.9380
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9849 - val_loss: 0.9530
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9984 - val_loss: 0.9344
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9998 - val_loss: 0.9507
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9932 - val_loss: 0.9100
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9979 - val_loss: 0.9213
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9924 - val_loss: 0.9535
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9995 - val_loss: 0.9622
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9911 - val_loss: 0.9137
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9758 - val_loss: 0.9394
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9873 - val_loss: 0.9264
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9914 - val_loss: 0.9229
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9881 - val_loss: 0.9056
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9803 - val_loss: 0.9208
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9885 - val_loss: 0.9301
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9757 - val_loss: 0.9455
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9822 - val_loss: 0.9353
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.27 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.29 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.21 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.21 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 6.47 seconds.
Calculated PHATE in 8.18 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_18 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_19 (Dense)                (None, 32)           2080        dense_18[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_19[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_19[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_20 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_21 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_22 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_18 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_19 (Dense)                (None, 32)           2080        dense_18[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_19[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_19[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_60 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_60 (T [()]                 0           tf_op_layer_Shape_60[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Pack_6 (TensorFlowO [(2,)]               0           tf_op_layer_strided_slice_60[0][0
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_6[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mul_18 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_6 (TensorFlowOp [(None, 3)]          0           tf_op_layer_Mul_18[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_61 (TensorFlo [(2,)]               0           tf_op_layer_Add_6[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_63 (TensorFlo [(2,)]               0           tf_op_layer_Add_6[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_62 (TensorFlo [(2,)]               0           tf_op_layer_Add_6[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_64 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_66 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_65 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_67 (TensorFlo [(2,)]               0           tf_op_layer_Add_6[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_69 (TensorFlo [(2,)]               0           tf_op_layer_Add_6[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_68 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_61 (T [()]                 0           tf_op_layer_Shape_61[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_63 (T [()]                 0           tf_op_layer_Shape_63[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_62 (T [()]                 0           tf_op_layer_Shape_62[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_64 (T [()]                 0           tf_op_layer_Shape_64[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_66 (T [()]                 0           tf_op_layer_Shape_66[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_65 (T [()]                 0           tf_op_layer_Shape_65[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_67 (T [()]                 0           tf_op_layer_Shape_67[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_69 (T [()]                 0           tf_op_layer_Shape_69[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_68 (T [()]                 0           tf_op_layer_Shape_68[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Reshape_36/shape (T [(3,)]               0           tf_op_layer_strided_slice_61[0][0
                                                                 tf_op_layer_strided_slice_63[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_37/shape (T [(3,)]               0           tf_op_layer_strided_slice_62[0][0
                                                                 tf_op_layer_strided_slice_63[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_38/shape (T [(3,)]               0           tf_op_layer_strided_slice_64[0][0
                                                                 tf_op_layer_strided_slice_66[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_39/shape (T [(3,)]               0           tf_op_layer_strided_slice_65[0][0
                                                                 tf_op_layer_strided_slice_66[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_40/shape (T [(3,)]               0           tf_op_layer_strided_slice_67[0][0
                                                                 tf_op_layer_strided_slice_69[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_41/shape (T [(3,)]               0           tf_op_layer_strided_slice_68[0][0
                                                                 tf_op_layer_strided_slice_69[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_36 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_6[0][0]          
                                                                 tf_op_layer_Reshape_36/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_36/multiples ( [(3,)]               0           tf_op_layer_strided_slice_62[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_37 (TensorF [(1, None, None)]    0           tf_op_layer_Add_6[0][0]          
                                                                 tf_op_layer_Reshape_37/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_37/multiples ( [(3,)]               0           tf_op_layer_strided_slice_61[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_38 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_38/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_38/multiples ( [(3,)]               0           tf_op_layer_strided_slice_65[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_39 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_39/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_39/multiples ( [(3,)]               0           tf_op_layer_strided_slice_64[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_40 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_6[0][0]          
                                                                 tf_op_layer_Reshape_40/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_40/multiples ( [(3,)]               0           tf_op_layer_strided_slice_68[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_41 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_41/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_41/multiples ( [(3,)]               0           tf_op_layer_strided_slice_67[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_36 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_36[0][0]     
                                                                 tf_op_layer_Tile_36/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_37 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_37[0][0]     
                                                                 tf_op_layer_Tile_37/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_38 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_38[0][0]     
                                                                 tf_op_layer_Tile_38/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_39 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_39[0][0]     
                                                                 tf_op_layer_Tile_39/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_40 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_40[0][0]     
                                                                 tf_op_layer_Tile_40/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_41 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_41[0][0]     
                                                                 tf_op_layer_Tile_41/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_30 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_36[0][0]        
                                                                 tf_op_layer_Tile_37[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_31 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_38[0][0]        
                                                                 tf_op_layer_Tile_39[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_32 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_40[0][0]        
                                                                 tf_op_layer_Tile_41[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_24 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_30[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_25 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_31[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_26 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_32[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_42 (TensorFlow [(None, None)]       0           tf_op_layer_Square_24[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_43 (TensorFlow [(None, None)]       0           tf_op_layer_Square_25[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_44 (TensorFlow [(None, None)]       0           tf_op_layer_Square_26[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_18 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_42[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_18 (TensorFlow [()]                 0           tf_op_layer_strided_slice_63[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_19 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_43[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_19 (TensorFlow [()]                 0           tf_op_layer_strided_slice_66[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_20 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_44[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_20 (TensorFlow [()]                 0           tf_op_layer_strided_slice_69[0][0
__________________________________________________________________________________________________
tf_op_layer_RealDiv_18 (TensorF [(None, None)]       0           tf_op_layer_Neg_18[0][0]         
                                                                 tf_op_layer_Cast_18[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_19 (TensorF [(None, None)]       0           tf_op_layer_Neg_19[0][0]         
                                                                 tf_op_layer_Cast_19[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_20 (TensorF [(None, None)]       0           tf_op_layer_Neg_20[0][0]         
                                                                 tf_op_layer_Cast_20[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_18 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_18[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_19 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_19[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_20 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_20[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_45 (TensorFlow [()]                 0           tf_op_layer_Exp_18[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_46 (TensorFlow [()]                 0           tf_op_layer_Exp_19[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_47 (TensorFlow [()]                 0           tf_op_layer_Exp_20[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_34 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_12 (TensorFlo [()]                 0           tf_op_layer_Mean_45[0][0]        
                                                                 tf_op_layer_Mean_46[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_19 (TensorFlowO [()]                 0           tf_op_layer_Mean_47[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_27 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_34[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_33 (TensorFlowO [()]                 0           tf_op_layer_AddV2_12[0][0]       
                                                                 tf_op_layer_Mul_19[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_6 (TensorFlowOp [(None,)]            0           tf_op_layer_Square_27[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_20 (TensorFlowO [()]                 0           tf_op_layer_Sub_33[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_13 (TensorFlo [(None,)]            0           tf_op_layer_Sum_6[0][0]          
                                                                 tf_op_layer_Mul_20[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_48 (TensorFlow [()]                 0           tf_op_layer_AddV2_13[0][0]       
__________________________________________________________________________________________________
add_loss_6 (AddLoss)            ()                   0           tf_op_layer_Mean_48[0][0]        
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 9.6535 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.2355s). Check your callbacks.
24/24 [==============================] - 1s 27ms/step - loss: 4.0942 - val_loss: 1.8530
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4246 - val_loss: 1.3848
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1959 - val_loss: 1.3907
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0160 - val_loss: 1.0818
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9135 - val_loss: 1.0287
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8645 - val_loss: 1.0251
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8423 - val_loss: 0.9971
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8334 - val_loss: 0.9539
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8104 - val_loss: 0.9456
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7909 - val_loss: 0.9342
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7952 - val_loss: 0.9371
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7994 - val_loss: 0.9259
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7747 - val_loss: 0.9304
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7802 - val_loss: 0.9213
Epoch 15/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7791 - val_loss: 0.9197
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7717 - val_loss: 0.9140
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7723 - val_loss: 0.8960
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7603 - val_loss: 0.9253
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7673 - val_loss: 0.8779
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7552 - val_loss: 0.8817
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7600 - val_loss: 0.9111
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7605 - val_loss: 0.8694
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7464 - val_loss: 0.8833
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7482 - val_loss: 0.8692
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7476 - val_loss: 0.8599
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7540 - val_loss: 0.8693
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7448 - val_loss: 0.8764
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7392 - val_loss: 0.8584
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7391 - val_loss: 0.8653
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7358 - val_loss: 0.8813
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7319 - val_loss: 0.8668
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7324 - val_loss: 0.8927
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7416 - val_loss: 0.8540
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7232 - val_loss: 0.8550
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7172 - val_loss: 0.8544
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7323 - val_loss: 0.8567
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7193 - val_loss: 0.8487
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7231 - val_loss: 0.8478
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7296 - val_loss: 0.8430
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7311 - val_loss: 0.8310
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7136 - val_loss: 0.8450
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7234 - val_loss: 0.8391
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7295 - val_loss: 0.8549
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7253 - val_loss: 0.8606
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7064 - val_loss: 0.8357
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7110 - val_loss: 0.8282
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7079 - val_loss: 0.8817
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7132 - val_loss: 0.8382
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7045 - val_loss: 0.8400
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7084 - val_loss: 0.8280
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7152 - val_loss: 0.8212
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7049 - val_loss: 0.8312
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7048 - val_loss: 0.8287
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7066 - val_loss: 0.8609
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6980 - val_loss: 0.8276
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6982 - val_loss: 0.8287
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7032 - val_loss: 0.8407
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6961 - val_loss: 0.8458
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6917 - val_loss: 0.8263
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6916 - val_loss: 0.8188
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6855 - val_loss: 0.8252
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6903 - val_loss: 0.8282
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6938 - val_loss: 0.8319
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6906 - val_loss: 0.8241
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7085 - val_loss: 0.8220
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6878 - val_loss: 0.8313
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6945 - val_loss: 0.8132
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6797 - val_loss: 0.8226
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6865 - val_loss: 0.8126
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6891 - val_loss: 0.8308
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6836 - val_loss: 0.8166
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6887 - val_loss: 0.8150
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6826 - val_loss: 0.8315
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6933 - val_loss: 0.8359
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6781 - val_loss: 0.8125
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6883 - val_loss: 0.8164
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6706 - val_loss: 0.8193
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6791 - val_loss: 0.8266
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6834 - val_loss: 0.8247
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6810 - val_loss: 0.8071
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6800 - val_loss: 0.8148
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6831 - val_loss: 0.8294
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6683 - val_loss: 0.8223
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6696 - val_loss: 0.8160
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6815 - val_loss: 0.8248
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6823 - val_loss: 0.8260
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6789 - val_loss: 0.8217
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6789 - val_loss: 0.8089
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6738 - val_loss: 0.8073
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6748 - val_loss: 0.8258
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6740 - val_loss: 0.8260
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6771 - val_loss: 0.8114
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6650 - val_loss: 0.8124
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6708 - val_loss: 0.8161
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6710 - val_loss: 0.8058
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6725 - val_loss: 0.8112
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6742 - val_loss: 0.8100
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6641 - val_loss: 0.8079
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6684 - val_loss: 0.8063
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6590 - val_loss: 0.8122
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_23 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_70 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_70 (T [()]                 0           tf_op_layer_Shape_70[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Pack_7 (TensorFlowO [(2,)]               0           tf_op_layer_strided_slice_70[0][0
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_7[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mul_21 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_7 (TensorFlowOp [(None, 3)]          0           tf_op_layer_Mul_21[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_71 (TensorFlo [(2,)]               0           tf_op_layer_Add_7[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_73 (TensorFlo [(2,)]               0           tf_op_layer_Add_7[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_72 (TensorFlo [(2,)]               0           tf_op_layer_Add_7[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_74 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_76 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_75 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_77 (TensorFlo [(2,)]               0           tf_op_layer_Add_7[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_79 (TensorFlo [(2,)]               0           tf_op_layer_Add_7[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_78 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_71 (T [()]                 0           tf_op_layer_Shape_71[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_73 (T [()]                 0           tf_op_layer_Shape_73[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_72 (T [()]                 0           tf_op_layer_Shape_72[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_74 (T [()]                 0           tf_op_layer_Shape_74[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_76 (T [()]                 0           tf_op_layer_Shape_76[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_75 (T [()]                 0           tf_op_layer_Shape_75[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_77 (T [()]                 0           tf_op_layer_Shape_77[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_79 (T [()]                 0           tf_op_layer_Shape_79[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_78 (T [()]                 0           tf_op_layer_Shape_78[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Reshape_42/shape (T [(3,)]               0           tf_op_layer_strided_slice_71[0][0
                                                                 tf_op_layer_strided_slice_73[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_43/shape (T [(3,)]               0           tf_op_layer_strided_slice_72[0][0
                                                                 tf_op_layer_strided_slice_73[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_44/shape (T [(3,)]               0           tf_op_layer_strided_slice_74[0][0
                                                                 tf_op_layer_strided_slice_76[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_45/shape (T [(3,)]               0           tf_op_layer_strided_slice_75[0][0
                                                                 tf_op_layer_strided_slice_76[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_46/shape (T [(3,)]               0           tf_op_layer_strided_slice_77[0][0
                                                                 tf_op_layer_strided_slice_79[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_47/shape (T [(3,)]               0           tf_op_layer_strided_slice_78[0][0
                                                                 tf_op_layer_strided_slice_79[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_42 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_7[0][0]          
                                                                 tf_op_layer_Reshape_42/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_42/multiples ( [(3,)]               0           tf_op_layer_strided_slice_72[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_43 (TensorF [(1, None, None)]    0           tf_op_layer_Add_7[0][0]          
                                                                 tf_op_layer_Reshape_43/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_43/multiples ( [(3,)]               0           tf_op_layer_strided_slice_71[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_44 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_44/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_44/multiples ( [(3,)]               0           tf_op_layer_strided_slice_75[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_45 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_45/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_45/multiples ( [(3,)]               0           tf_op_layer_strided_slice_74[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_46 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_7[0][0]          
                                                                 tf_op_layer_Reshape_46/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_46/multiples ( [(3,)]               0           tf_op_layer_strided_slice_78[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_47 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_47/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_47/multiples ( [(3,)]               0           tf_op_layer_strided_slice_77[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_42 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_42[0][0]     
                                                                 tf_op_layer_Tile_42/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_43 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_43[0][0]     
                                                                 tf_op_layer_Tile_43/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_44 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_44[0][0]     
                                                                 tf_op_layer_Tile_44/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_45 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_45[0][0]     
                                                                 tf_op_layer_Tile_45/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_46 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_46[0][0]     
                                                                 tf_op_layer_Tile_46/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_47 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_47[0][0]     
                                                                 tf_op_layer_Tile_47/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_35 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_42[0][0]        
                                                                 tf_op_layer_Tile_43[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_36 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_44[0][0]        
                                                                 tf_op_layer_Tile_45[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_37 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_46[0][0]        
                                                                 tf_op_layer_Tile_47[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_28 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_35[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_29 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_36[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_30 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_37[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_49 (TensorFlow [(None, None)]       0           tf_op_layer_Square_28[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_50 (TensorFlow [(None, None)]       0           tf_op_layer_Square_29[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_51 (TensorFlow [(None, None)]       0           tf_op_layer_Square_30[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_21 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_49[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_21 (TensorFlow [()]                 0           tf_op_layer_strided_slice_73[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_22 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_50[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_22 (TensorFlow [()]                 0           tf_op_layer_strided_slice_76[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_23 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_51[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_23 (TensorFlow [()]                 0           tf_op_layer_strided_slice_79[0][0
__________________________________________________________________________________________________
tf_op_layer_RealDiv_21 (TensorF [(None, None)]       0           tf_op_layer_Neg_21[0][0]         
                                                                 tf_op_layer_Cast_21[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_22 (TensorF [(None, None)]       0           tf_op_layer_Neg_22[0][0]         
                                                                 tf_op_layer_Cast_22[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_23 (TensorF [(None, None)]       0           tf_op_layer_Neg_23[0][0]         
                                                                 tf_op_layer_Cast_23[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_21 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_21[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_22 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_22[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_23 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_23[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_52 (TensorFlow [()]                 0           tf_op_layer_Exp_21[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_53 (TensorFlow [()]                 0           tf_op_layer_Exp_22[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_54 (TensorFlow [()]                 0           tf_op_layer_Exp_23[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_39 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_14 (TensorFlo [()]                 0           tf_op_layer_Mean_52[0][0]        
                                                                 tf_op_layer_Mean_53[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_22 (TensorFlowO [()]                 0           tf_op_layer_Mean_54[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_31 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_39[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_38 (TensorFlowO [()]                 0           tf_op_layer_AddV2_14[0][0]       
                                                                 tf_op_layer_Mul_22[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_7 (TensorFlowOp [(None,)]            0           tf_op_layer_Square_31[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_23 (TensorFlowO [()]                 0           tf_op_layer_Sub_38[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_15 (TensorFlo [(None,)]            0           tf_op_layer_Sum_7[0][0]          
                                                                 tf_op_layer_Mul_23[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_55 (TensorFlow [()]                 0           tf_op_layer_AddV2_15[0][0]       
__________________________________________________________________________________________________
add_loss_7 (AddLoss)            ()                   0           tf_op_layer_Mean_55[0][0]        
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 0s - loss: 10.52 - ETA: 2s - loss: 10.7434WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.2074s). Check your callbacks.
24/24 [==============================] - 1s 25ms/step - loss: 8.5993 - val_loss: 6.1599
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.5905 - val_loss: 3.3708
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.6901 - val_loss: 2.4231
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 2.0539 - val_loss: 2.0927
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8457 - val_loss: 1.9251
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.7369 - val_loss: 1.8131
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6467 - val_loss: 1.7387
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5837 - val_loss: 1.6710
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5463 - val_loss: 1.6340
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4963 - val_loss: 1.5473
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4782 - val_loss: 1.4648
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4113 - val_loss: 1.4715
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3509 - val_loss: 1.3832
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3200 - val_loss: 1.3299
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2657 - val_loss: 1.3333
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2478 - val_loss: 1.2626
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2304 - val_loss: 1.2517
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2002 - val_loss: 1.2197
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1790 - val_loss: 1.2060
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1662 - val_loss: 1.2026
Epoch 21/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1717 - val_loss: 1.1761
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1426 - val_loss: 1.1793
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1350 - val_loss: 1.1704
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1199 - val_loss: 1.1444
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1159 - val_loss: 1.1558
Epoch 26/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1132 - val_loss: 1.1743
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0968 - val_loss: 1.1548
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0876 - val_loss: 1.1176
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0814 - val_loss: 1.1361
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0788 - val_loss: 1.1361
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0823 - val_loss: 1.1321
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0695 - val_loss: 1.1402
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0731 - val_loss: 1.1073
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0642 - val_loss: 1.1547
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0627 - val_loss: 1.1100
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0530 - val_loss: 1.1108
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0603 - val_loss: 1.1100
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0555 - val_loss: 1.0961
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0436 - val_loss: 1.0916
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0448 - val_loss: 1.1226
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0477 - val_loss: 1.0952
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0486 - val_loss: 1.1122
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0346 - val_loss: 1.0669
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0372 - val_loss: 1.1021
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0289 - val_loss: 1.0919
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0241 - val_loss: 1.1023
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0300 - val_loss: 1.0778
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0179 - val_loss: 1.0844
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0284 - val_loss: 1.0824
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0213 - val_loss: 1.0966
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0252 - val_loss: 1.0853
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0075 - val_loss: 1.0746
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0138 - val_loss: 1.0866
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0234 - val_loss: 1.0892
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0255 - val_loss: 1.0744
Epoch 56/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0261 - val_loss: 1.0816
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0124 - val_loss: 1.0756
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0109 - val_loss: 1.0605
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0059 - val_loss: 1.0659
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0160 - val_loss: 1.0554
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0147 - val_loss: 1.0597
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0013 - val_loss: 1.0899
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0030 - val_loss: 1.0867
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0023 - val_loss: 1.0581
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9916 - val_loss: 1.0646
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0047 - val_loss: 1.0477
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0011 - val_loss: 1.0692
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9948 - val_loss: 1.0880
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9967 - val_loss: 1.0828
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9957 - val_loss: 1.0609
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9923 - val_loss: 1.0504
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0019 - val_loss: 1.0680
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0008 - val_loss: 1.0548
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9961 - val_loss: 1.0547
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9912 - val_loss: 1.0755
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9981 - val_loss: 1.0768
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0058 - val_loss: 1.0568
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9965 - val_loss: 1.0623
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9857 - val_loss: 1.0608
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9931 - val_loss: 1.0784
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9780 - val_loss: 1.0504
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9866 - val_loss: 1.0555
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9800 - val_loss: 1.0702
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9840 - val_loss: 1.0440
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9925 - val_loss: 1.0505
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9941 - val_loss: 1.0482
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9891 - val_loss: 1.0616
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9982 - val_loss: 1.0457
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9848 - val_loss: 1.0641
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9686 - val_loss: 1.0804
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9776 - val_loss: 1.0508
Epoch 92/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9744 - val_loss: 1.0484
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9703 - val_loss: 1.0690
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9827 - val_loss: 1.0657
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9802 - val_loss: 1.0415
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9776 - val_loss: 1.0584
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9796 - val_loss: 1.0642
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9844 - val_loss: 1.0488
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9897 - val_loss: 1.0479
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9781 - val_loss: 1.0460
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.47 seconds.
    Calculating affinities...
    Calculated affinities in 0.07 seconds.
  Calculated graph and diffusion operator in 0.56 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 5.80 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.36 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 10.52 seconds.
Calculated PHATE in 17.26 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_24 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_25 (Dense)                (None, 32)           2080        dense_24[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_25[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_25[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_26 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_27 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_28 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_24 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_25 (Dense)                (None, 32)           2080        dense_24[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_25[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_25[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_80 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_80 (T [()]                 0           tf_op_layer_Shape_80[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Pack_8 (TensorFlowO [(2,)]               0           tf_op_layer_strided_slice_80[0][0
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_8[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mul_24 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_8 (TensorFlowOp [(None, 3)]          0           tf_op_layer_Mul_24[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_81 (TensorFlo [(2,)]               0           tf_op_layer_Add_8[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_83 (TensorFlo [(2,)]               0           tf_op_layer_Add_8[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_82 (TensorFlo [(2,)]               0           tf_op_layer_Add_8[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_84 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_86 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_85 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_87 (TensorFlo [(2,)]               0           tf_op_layer_Add_8[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_89 (TensorFlo [(2,)]               0           tf_op_layer_Add_8[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_88 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_81 (T [()]                 0           tf_op_layer_Shape_81[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_83 (T [()]                 0           tf_op_layer_Shape_83[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_82 (T [()]                 0           tf_op_layer_Shape_82[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_84 (T [()]                 0           tf_op_layer_Shape_84[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_86 (T [()]                 0           tf_op_layer_Shape_86[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_85 (T [()]                 0           tf_op_layer_Shape_85[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_87 (T [()]                 0           tf_op_layer_Shape_87[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_89 (T [()]                 0           tf_op_layer_Shape_89[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_88 (T [()]                 0           tf_op_layer_Shape_88[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Reshape_48/shape (T [(3,)]               0           tf_op_layer_strided_slice_81[0][0
                                                                 tf_op_layer_strided_slice_83[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_49/shape (T [(3,)]               0           tf_op_layer_strided_slice_82[0][0
                                                                 tf_op_layer_strided_slice_83[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_50/shape (T [(3,)]               0           tf_op_layer_strided_slice_84[0][0
                                                                 tf_op_layer_strided_slice_86[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_51/shape (T [(3,)]               0           tf_op_layer_strided_slice_85[0][0
                                                                 tf_op_layer_strided_slice_86[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_52/shape (T [(3,)]               0           tf_op_layer_strided_slice_87[0][0
                                                                 tf_op_layer_strided_slice_89[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_53/shape (T [(3,)]               0           tf_op_layer_strided_slice_88[0][0
                                                                 tf_op_layer_strided_slice_89[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_48 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_8[0][0]          
                                                                 tf_op_layer_Reshape_48/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_48/multiples ( [(3,)]               0           tf_op_layer_strided_slice_82[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_49 (TensorF [(1, None, None)]    0           tf_op_layer_Add_8[0][0]          
                                                                 tf_op_layer_Reshape_49/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_49/multiples ( [(3,)]               0           tf_op_layer_strided_slice_81[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_50 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_50/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_50/multiples ( [(3,)]               0           tf_op_layer_strided_slice_85[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_51 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_51/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_51/multiples ( [(3,)]               0           tf_op_layer_strided_slice_84[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_52 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_8[0][0]          
                                                                 tf_op_layer_Reshape_52/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_52/multiples ( [(3,)]               0           tf_op_layer_strided_slice_88[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_53 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_53/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_53/multiples ( [(3,)]               0           tf_op_layer_strided_slice_87[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_48 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_48[0][0]     
                                                                 tf_op_layer_Tile_48/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_49 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_49[0][0]     
                                                                 tf_op_layer_Tile_49/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_50 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_50[0][0]     
                                                                 tf_op_layer_Tile_50/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_51 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_51[0][0]     
                                                                 tf_op_layer_Tile_51/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_52 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_52[0][0]     
                                                                 tf_op_layer_Tile_52/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_53 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_53[0][0]     
                                                                 tf_op_layer_Tile_53/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_40 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_48[0][0]        
                                                                 tf_op_layer_Tile_49[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_41 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_50[0][0]        
                                                                 tf_op_layer_Tile_51[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_42 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_52[0][0]        
                                                                 tf_op_layer_Tile_53[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_32 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_40[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_33 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_41[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_34 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_42[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_56 (TensorFlow [(None, None)]       0           tf_op_layer_Square_32[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_57 (TensorFlow [(None, None)]       0           tf_op_layer_Square_33[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_58 (TensorFlow [(None, None)]       0           tf_op_layer_Square_34[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_24 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_56[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_24 (TensorFlow [()]                 0           tf_op_layer_strided_slice_83[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_25 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_57[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_25 (TensorFlow [()]                 0           tf_op_layer_strided_slice_86[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_26 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_58[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_26 (TensorFlow [()]                 0           tf_op_layer_strided_slice_89[0][0
__________________________________________________________________________________________________
tf_op_layer_RealDiv_24 (TensorF [(None, None)]       0           tf_op_layer_Neg_24[0][0]         
                                                                 tf_op_layer_Cast_24[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_25 (TensorF [(None, None)]       0           tf_op_layer_Neg_25[0][0]         
                                                                 tf_op_layer_Cast_25[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_26 (TensorF [(None, None)]       0           tf_op_layer_Neg_26[0][0]         
                                                                 tf_op_layer_Cast_26[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_24 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_24[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_25 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_25[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_26 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_26[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_59 (TensorFlow [()]                 0           tf_op_layer_Exp_24[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_60 (TensorFlow [()]                 0           tf_op_layer_Exp_25[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_61 (TensorFlow [()]                 0           tf_op_layer_Exp_26[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_44 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_16 (TensorFlo [()]                 0           tf_op_layer_Mean_59[0][0]        
                                                                 tf_op_layer_Mean_60[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_25 (TensorFlowO [()]                 0           tf_op_layer_Mean_61[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_35 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_44[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_43 (TensorFlowO [()]                 0           tf_op_layer_AddV2_16[0][0]       
                                                                 tf_op_layer_Mul_25[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_8 (TensorFlowOp [(None,)]            0           tf_op_layer_Square_35[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_26 (TensorFlowO [()]                 0           tf_op_layer_Sub_43[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_17 (TensorFlo [(None,)]            0           tf_op_layer_Sum_8[0][0]          
                                                                 tf_op_layer_Mul_26[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_62 (TensorFlow [()]                 0           tf_op_layer_AddV2_17[0][0]       
__________________________________________________________________________________________________
add_loss_8 (AddLoss)            ()                   0           tf_op_layer_Mean_62[0][0]        
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 4s - loss: 10.3176WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.3614s). Check your callbacks.
24/24 [==============================] - 1s 36ms/step - loss: 4.1362 - val_loss: 2.0168
Epoch 2/100
24/24 [==============================] - 0s 4ms/step - loss: 1.5575 - val_loss: 1.5146
Epoch 3/100
24/24 [==============================] - 0s 4ms/step - loss: 1.2606 - val_loss: 1.3823
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2175 - val_loss: 1.2597
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1185 - val_loss: 1.1684
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0429 - val_loss: 1.1154
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9688 - val_loss: 0.9548
Epoch 8/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8874 - val_loss: 0.9178
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8609 - val_loss: 0.9037
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8467 - val_loss: 0.8577
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8363 - val_loss: 0.8730
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8338 - val_loss: 0.8657
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8330 - val_loss: 0.8402
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8153 - val_loss: 0.8591
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8116 - val_loss: 0.8646
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7959 - val_loss: 0.8475
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7855 - val_loss: 0.8497
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7849 - val_loss: 0.8511
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7935 - val_loss: 0.8287
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7867 - val_loss: 0.8225
Epoch 21/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7867 - val_loss: 0.8286
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7728 - val_loss: 0.8032
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7746 - val_loss: 0.8397
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7666 - val_loss: 0.8192
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7651 - val_loss: 0.8010
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7651 - val_loss: 0.7971
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7635 - val_loss: 0.8189
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7585 - val_loss: 0.8007
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7607 - val_loss: 0.7975
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7596 - val_loss: 0.8127
Epoch 31/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7544 - val_loss: 0.8008
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7538 - val_loss: 0.7865
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7438 - val_loss: 0.7953
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7471 - val_loss: 0.7916
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7452 - val_loss: 0.7814
Epoch 36/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7384 - val_loss: 0.7810
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7413 - val_loss: 0.8199
Epoch 38/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7281 - val_loss: 0.8134
Epoch 39/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7438 - val_loss: 0.7734
Epoch 40/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7225 - val_loss: 0.7906
Epoch 41/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7225 - val_loss: 0.7851
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7276 - val_loss: 0.7954
Epoch 43/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7266 - val_loss: 0.7740
Epoch 44/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7280 - val_loss: 0.7805
Epoch 45/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7237 - val_loss: 0.7658
Epoch 46/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7224 - val_loss: 0.7647
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7270 - val_loss: 0.7563
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7099 - val_loss: 0.7556
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7113 - val_loss: 0.7591
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7238 - val_loss: 0.7466
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7143 - val_loss: 0.7951
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7154 - val_loss: 0.7626
Epoch 53/100
24/24 [==============================] - 0s 6ms/step - loss: 0.7043 - val_loss: 0.7759
Epoch 54/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6994 - val_loss: 0.7424
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7091 - val_loss: 0.7753
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7094 - val_loss: 0.7764
Epoch 57/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7012 - val_loss: 0.7634
Epoch 58/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7011 - val_loss: 0.7491
Epoch 59/100
24/24 [==============================] - 0s 5ms/step - loss: 0.6924 - val_loss: 0.7477
Epoch 60/100
24/24 [==============================] - 0s 5ms/step - loss: 0.6946 - val_loss: 0.7444
Epoch 61/100
24/24 [==============================] - 0s 7ms/step - loss: 0.7115 - val_loss: 0.7566
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6925 - val_loss: 0.7601
Epoch 63/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7043 - val_loss: 0.7384
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7000 - val_loss: 0.7392
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6924 - val_loss: 0.7367
Epoch 66/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6899 - val_loss: 0.7366
Epoch 67/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6931 - val_loss: 0.7422
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6933 - val_loss: 0.7966
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6898 - val_loss: 0.7388
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6885 - val_loss: 0.7299
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6964 - val_loss: 0.7371
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6921 - val_loss: 0.7513
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6924 - val_loss: 0.7402
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6828 - val_loss: 0.7383
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6884 - val_loss: 0.7520
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6790 - val_loss: 0.7608
Epoch 77/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6799 - val_loss: 0.7318
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6866 - val_loss: 0.7415
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6987 - val_loss: 0.7354
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6813 - val_loss: 0.7442
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6899 - val_loss: 0.7790
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6866 - val_loss: 0.7307
Epoch 83/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6816 - val_loss: 0.7869
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6843 - val_loss: 0.7481
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6735 - val_loss: 0.7243
Epoch 86/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6833 - val_loss: 0.7369
Epoch 87/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6732 - val_loss: 0.7409
Epoch 88/100
24/24 [==============================] - 0s 5ms/step - loss: 0.6695 - val_loss: 0.7371
Epoch 89/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6720 - val_loss: 0.7222
Epoch 90/100
24/24 [==============================] - 0s 5ms/step - loss: 0.6748 - val_loss: 0.7906
Epoch 91/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6864 - val_loss: 0.7589
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6780 - val_loss: 0.7288
Epoch 93/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6721 - val_loss: 0.7258
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6768 - val_loss: 0.7385
Epoch 95/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6731 - val_loss: 0.7169
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6702 - val_loss: 0.7256
Epoch 97/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6693 - val_loss: 0.7803
Epoch 98/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6730 - val_loss: 0.7483
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6834 - val_loss: 0.7468
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6732 - val_loss: 0.7263
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_29 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_90 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_90 (T [()]                 0           tf_op_layer_Shape_90[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Pack_9 (TensorFlowO [(2,)]               0           tf_op_layer_strided_slice_90[0][0
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_9[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mul_27 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_9 (TensorFlowOp [(None, 3)]          0           tf_op_layer_Mul_27[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_91 (TensorFlo [(2,)]               0           tf_op_layer_Add_9[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_93 (TensorFlo [(2,)]               0           tf_op_layer_Add_9[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_92 (TensorFlo [(2,)]               0           tf_op_layer_Add_9[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_94 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_96 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_95 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_97 (TensorFlo [(2,)]               0           tf_op_layer_Add_9[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_99 (TensorFlo [(2,)]               0           tf_op_layer_Add_9[0][0]          
__________________________________________________________________________________________________
tf_op_layer_Shape_98 (TensorFlo [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_91 (T [()]                 0           tf_op_layer_Shape_91[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_93 (T [()]                 0           tf_op_layer_Shape_93[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_92 (T [()]                 0           tf_op_layer_Shape_92[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_94 (T [()]                 0           tf_op_layer_Shape_94[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_96 (T [()]                 0           tf_op_layer_Shape_96[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_95 (T [()]                 0           tf_op_layer_Shape_95[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_97 (T [()]                 0           tf_op_layer_Shape_97[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_99 (T [()]                 0           tf_op_layer_Shape_99[0][0]       
__________________________________________________________________________________________________
tf_op_layer_strided_slice_98 (T [()]                 0           tf_op_layer_Shape_98[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Reshape_54/shape (T [(3,)]               0           tf_op_layer_strided_slice_91[0][0
                                                                 tf_op_layer_strided_slice_93[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_55/shape (T [(3,)]               0           tf_op_layer_strided_slice_92[0][0
                                                                 tf_op_layer_strided_slice_93[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_56/shape (T [(3,)]               0           tf_op_layer_strided_slice_94[0][0
                                                                 tf_op_layer_strided_slice_96[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_57/shape (T [(3,)]               0           tf_op_layer_strided_slice_95[0][0
                                                                 tf_op_layer_strided_slice_96[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_58/shape (T [(3,)]               0           tf_op_layer_strided_slice_97[0][0
                                                                 tf_op_layer_strided_slice_99[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_59/shape (T [(3,)]               0           tf_op_layer_strided_slice_98[0][0
                                                                 tf_op_layer_strided_slice_99[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_54 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_9[0][0]          
                                                                 tf_op_layer_Reshape_54/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_54/multiples ( [(3,)]               0           tf_op_layer_strided_slice_92[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_55 (TensorF [(1, None, None)]    0           tf_op_layer_Add_9[0][0]          
                                                                 tf_op_layer_Reshape_55/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_55/multiples ( [(3,)]               0           tf_op_layer_strided_slice_91[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_56 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_56/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_56/multiples ( [(3,)]               0           tf_op_layer_strided_slice_95[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_57 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_57/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_57/multiples ( [(3,)]               0           tf_op_layer_strided_slice_94[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_58 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_9[0][0]          
                                                                 tf_op_layer_Reshape_58/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_58/multiples ( [(3,)]               0           tf_op_layer_strided_slice_98[0][0
__________________________________________________________________________________________________
tf_op_layer_Reshape_59 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_59/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_59/multiples ( [(3,)]               0           tf_op_layer_strided_slice_97[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_54 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_54[0][0]     
                                                                 tf_op_layer_Tile_54/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_55 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_55[0][0]     
                                                                 tf_op_layer_Tile_55/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_56 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_56[0][0]     
                                                                 tf_op_layer_Tile_56/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_57 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_57[0][0]     
                                                                 tf_op_layer_Tile_57/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_58 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_58[0][0]     
                                                                 tf_op_layer_Tile_58/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_59 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_59[0][0]     
                                                                 tf_op_layer_Tile_59/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_45 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_54[0][0]        
                                                                 tf_op_layer_Tile_55[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_46 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_56[0][0]        
                                                                 tf_op_layer_Tile_57[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_47 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_58[0][0]        
                                                                 tf_op_layer_Tile_59[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_36 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_45[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_37 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_46[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_38 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_47[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_63 (TensorFlow [(None, None)]       0           tf_op_layer_Square_36[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_64 (TensorFlow [(None, None)]       0           tf_op_layer_Square_37[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_65 (TensorFlow [(None, None)]       0           tf_op_layer_Square_38[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_27 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_63[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_27 (TensorFlow [()]                 0           tf_op_layer_strided_slice_93[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_28 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_64[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_28 (TensorFlow [()]                 0           tf_op_layer_strided_slice_96[0][0
__________________________________________________________________________________________________
tf_op_layer_Neg_29 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_65[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_29 (TensorFlow [()]                 0           tf_op_layer_strided_slice_99[0][0
__________________________________________________________________________________________________
tf_op_layer_RealDiv_27 (TensorF [(None, None)]       0           tf_op_layer_Neg_27[0][0]         
                                                                 tf_op_layer_Cast_27[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_28 (TensorF [(None, None)]       0           tf_op_layer_Neg_28[0][0]         
                                                                 tf_op_layer_Cast_28[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_29 (TensorF [(None, None)]       0           tf_op_layer_Neg_29[0][0]         
                                                                 tf_op_layer_Cast_29[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_27 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_27[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_28 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_28[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_29 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_29[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_66 (TensorFlow [()]                 0           tf_op_layer_Exp_27[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_67 (TensorFlow [()]                 0           tf_op_layer_Exp_28[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_68 (TensorFlow [()]                 0           tf_op_layer_Exp_29[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_49 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_18 (TensorFlo [()]                 0           tf_op_layer_Mean_66[0][0]        
                                                                 tf_op_layer_Mean_67[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_28 (TensorFlowO [()]                 0           tf_op_layer_Mean_68[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_39 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_49[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_48 (TensorFlowO [()]                 0           tf_op_layer_AddV2_18[0][0]       
                                                                 tf_op_layer_Mul_28[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_9 (TensorFlowOp [(None,)]            0           tf_op_layer_Square_39[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_29 (TensorFlowO [()]                 0           tf_op_layer_Sub_48[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_19 (TensorFlo [(None,)]            0           tf_op_layer_Sum_9[0][0]          
                                                                 tf_op_layer_Mul_29[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_69 (TensorFlow [()]                 0           tf_op_layer_AddV2_19[0][0]       
__________________________________________________________________________________________________
add_loss_9 (AddLoss)            ()                   0           tf_op_layer_Mean_69[0][0]        
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.5654WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.2130s). Check your callbacks.
24/24 [==============================] - 1s 28ms/step - loss: 8.1600 - val_loss: 6.1298
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 3.9625 - val_loss: 3.0571
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 2.4420 - val_loss: 2.2887
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.9910 - val_loss: 1.9830
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.7208 - val_loss: 1.7605
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5694 - val_loss: 1.6154
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4581 - val_loss: 1.5354
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3890 - val_loss: 1.4387
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3247 - val_loss: 1.4048
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3187 - val_loss: 1.4222
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2752 - val_loss: 1.3636
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2569 - val_loss: 1.3164
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2302 - val_loss: 1.3164
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2081 - val_loss: 1.2993
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2040 - val_loss: 1.2970
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1916 - val_loss: 1.2598
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1616 - val_loss: 1.2725
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1566 - val_loss: 1.2739
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1512 - val_loss: 1.2323
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1453 - val_loss: 1.2300
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1372 - val_loss: 1.2252
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1208 - val_loss: 1.1715
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1113 - val_loss: 1.2289
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1183 - val_loss: 1.2132
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1044 - val_loss: 1.1647
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1060 - val_loss: 1.1690
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0929 - val_loss: 1.2060
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1000 - val_loss: 1.1406
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0906 - val_loss: 1.2024
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0953 - val_loss: 1.1618
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0926 - val_loss: 1.1484
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0814 - val_loss: 1.1551
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0822 - val_loss: 1.2115
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0750 - val_loss: 1.1250
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0826 - val_loss: 1.1667
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0640 - val_loss: 1.1271
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0553 - val_loss: 1.1232
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0462 - val_loss: 1.1252
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0552 - val_loss: 1.1438
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0383 - val_loss: 1.1631
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0596 - val_loss: 1.1123
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0392 - val_loss: 1.1571
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0659 - val_loss: 1.1189
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0437 - val_loss: 1.0965
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0478 - val_loss: 1.0911
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0488 - val_loss: 1.0821
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0522 - val_loss: 1.0833
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0408 - val_loss: 1.0846
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0441 - val_loss: 1.1013
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0450 - val_loss: 1.1543
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0445 - val_loss: 1.1160
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0335 - val_loss: 1.1033
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0447 - val_loss: 1.1067
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0281 - val_loss: 1.1603
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0294 - val_loss: 1.0845
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0276 - val_loss: 1.0971
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0216 - val_loss: 1.1198
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0246 - val_loss: 1.0847
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0205 - val_loss: 1.0960
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0199 - val_loss: 1.1263
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0172 - val_loss: 1.0928
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0171 - val_loss: 1.1092
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0167 - val_loss: 1.0801
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0209 - val_loss: 1.1734
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0204 - val_loss: 1.1128
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0104 - val_loss: 1.0811
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0308 - val_loss: 1.0891
Epoch 68/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0158 - val_loss: 1.1040
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9893 - val_loss: 1.0859
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0261 - val_loss: 1.1362
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0100 - val_loss: 1.0583
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0022 - val_loss: 1.0841
Epoch 73/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0094 - val_loss: 1.0561
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0071 - val_loss: 1.1230
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0172 - val_loss: 1.1097
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0032 - val_loss: 1.0456
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9973 - val_loss: 1.0705
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9947 - val_loss: 1.0954
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0094 - val_loss: 1.1261
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0032 - val_loss: 1.0492
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9914 - val_loss: 1.0544
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0180 - val_loss: 1.0444
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0006 - val_loss: 1.0289
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0066 - val_loss: 1.1106
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0045 - val_loss: 1.0696
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9968 - val_loss: 1.0711
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0072 - val_loss: 1.0635
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9977 - val_loss: 1.0573
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0166 - val_loss: 1.0954
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9880 - val_loss: 1.0656
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9886 - val_loss: 1.0448
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0051 - val_loss: 1.0564
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0129 - val_loss: 1.0434
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9881 - val_loss: 1.0941
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9835 - val_loss: 1.0328
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0155 - val_loss: 1.0380
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9899 - val_loss: 1.0410
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9891 - val_loss: 1.0382
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9903 - val_loss: 1.0594
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9952 - val_loss: 1.0375
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.29 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.31 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.30 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.23 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 6.43 seconds.
Calculated PHATE in 8.29 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_30 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_31 (Dense)                (None, 32)           2080        dense_30[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_31[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_31[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_32 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_33 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_34 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_30 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_31 (Dense)                (None, 32)           2080        dense_30[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_31[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_31[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_100 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_100 ( [()]                 0           tf_op_layer_Shape_100[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_10 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_100[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_10[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_30 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_10 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_30[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_101 (TensorFl [(2,)]               0           tf_op_layer_Add_10[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_103 (TensorFl [(2,)]               0           tf_op_layer_Add_10[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_102 (TensorFl [(2,)]               0           tf_op_layer_Add_10[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_104 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_106 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_105 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_107 (TensorFl [(2,)]               0           tf_op_layer_Add_10[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_109 (TensorFl [(2,)]               0           tf_op_layer_Add_10[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_108 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_101 ( [()]                 0           tf_op_layer_Shape_101[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_103 ( [()]                 0           tf_op_layer_Shape_103[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_102 ( [()]                 0           tf_op_layer_Shape_102[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_104 ( [()]                 0           tf_op_layer_Shape_104[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_106 ( [()]                 0           tf_op_layer_Shape_106[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_105 ( [()]                 0           tf_op_layer_Shape_105[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_107 ( [()]                 0           tf_op_layer_Shape_107[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_109 ( [()]                 0           tf_op_layer_Shape_109[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_108 ( [()]                 0           tf_op_layer_Shape_108[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_60/shape (T [(3,)]               0           tf_op_layer_strided_slice_101[0][
                                                                 tf_op_layer_strided_slice_103[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_61/shape (T [(3,)]               0           tf_op_layer_strided_slice_102[0][
                                                                 tf_op_layer_strided_slice_103[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_62/shape (T [(3,)]               0           tf_op_layer_strided_slice_104[0][
                                                                 tf_op_layer_strided_slice_106[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_63/shape (T [(3,)]               0           tf_op_layer_strided_slice_105[0][
                                                                 tf_op_layer_strided_slice_106[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_64/shape (T [(3,)]               0           tf_op_layer_strided_slice_107[0][
                                                                 tf_op_layer_strided_slice_109[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_65/shape (T [(3,)]               0           tf_op_layer_strided_slice_108[0][
                                                                 tf_op_layer_strided_slice_109[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_60 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_10[0][0]         
                                                                 tf_op_layer_Reshape_60/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_60/multiples ( [(3,)]               0           tf_op_layer_strided_slice_102[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_61 (TensorF [(1, None, None)]    0           tf_op_layer_Add_10[0][0]         
                                                                 tf_op_layer_Reshape_61/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_61/multiples ( [(3,)]               0           tf_op_layer_strided_slice_101[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_62 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_62/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_62/multiples ( [(3,)]               0           tf_op_layer_strided_slice_105[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_63 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_63/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_63/multiples ( [(3,)]               0           tf_op_layer_strided_slice_104[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_64 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_10[0][0]         
                                                                 tf_op_layer_Reshape_64/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_64/multiples ( [(3,)]               0           tf_op_layer_strided_slice_108[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_65 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_65/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_65/multiples ( [(3,)]               0           tf_op_layer_strided_slice_107[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_60 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_60[0][0]     
                                                                 tf_op_layer_Tile_60/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_61 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_61[0][0]     
                                                                 tf_op_layer_Tile_61/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_62 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_62[0][0]     
                                                                 tf_op_layer_Tile_62/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_63 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_63[0][0]     
                                                                 tf_op_layer_Tile_63/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_64 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_64[0][0]     
                                                                 tf_op_layer_Tile_64/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_65 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_65[0][0]     
                                                                 tf_op_layer_Tile_65/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_50 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_60[0][0]        
                                                                 tf_op_layer_Tile_61[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_51 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_62[0][0]        
                                                                 tf_op_layer_Tile_63[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_52 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_64[0][0]        
                                                                 tf_op_layer_Tile_65[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_40 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_50[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_41 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_51[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_42 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_52[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_70 (TensorFlow [(None, None)]       0           tf_op_layer_Square_40[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_71 (TensorFlow [(None, None)]       0           tf_op_layer_Square_41[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_72 (TensorFlow [(None, None)]       0           tf_op_layer_Square_42[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_30 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_70[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_30 (TensorFlow [()]                 0           tf_op_layer_strided_slice_103[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_31 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_71[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_31 (TensorFlow [()]                 0           tf_op_layer_strided_slice_106[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_32 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_72[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_32 (TensorFlow [()]                 0           tf_op_layer_strided_slice_109[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_30 (TensorF [(None, None)]       0           tf_op_layer_Neg_30[0][0]         
                                                                 tf_op_layer_Cast_30[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_31 (TensorF [(None, None)]       0           tf_op_layer_Neg_31[0][0]         
                                                                 tf_op_layer_Cast_31[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_32 (TensorF [(None, None)]       0           tf_op_layer_Neg_32[0][0]         
                                                                 tf_op_layer_Cast_32[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_30 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_30[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_31 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_31[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_32 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_32[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_73 (TensorFlow [()]                 0           tf_op_layer_Exp_30[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_74 (TensorFlow [()]                 0           tf_op_layer_Exp_31[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_75 (TensorFlow [()]                 0           tf_op_layer_Exp_32[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_54 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_20 (TensorFlo [()]                 0           tf_op_layer_Mean_73[0][0]        
                                                                 tf_op_layer_Mean_74[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_31 (TensorFlowO [()]                 0           tf_op_layer_Mean_75[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_43 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_54[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_53 (TensorFlowO [()]                 0           tf_op_layer_AddV2_20[0][0]       
                                                                 tf_op_layer_Mul_31[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_10 (TensorFlowO [(None,)]            0           tf_op_layer_Square_43[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_32 (TensorFlowO [()]                 0           tf_op_layer_Sub_53[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_21 (TensorFlo [(None,)]            0           tf_op_layer_Sum_10[0][0]         
                                                                 tf_op_layer_Mul_32[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_76 (TensorFlow [()]                 0           tf_op_layer_AddV2_21[0][0]       
__________________________________________________________________________________________________
add_loss_10 (AddLoss)           ()                   0           tf_op_layer_Mean_76[0][0]        
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.8985 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.1264s). Check your callbacks.
24/24 [==============================] - 0s 15ms/step - loss: 4.4932 - val_loss: 2.3438
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6698 - val_loss: 1.6928
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2500 - val_loss: 1.4800
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1556 - val_loss: 1.3973
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9827 - val_loss: 1.2302
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9012 - val_loss: 1.1679
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8911 - val_loss: 1.1298
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8527 - val_loss: 1.0752
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8261 - val_loss: 1.0844
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8282 - val_loss: 1.1182
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8209 - val_loss: 1.0505
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8147 - val_loss: 1.0750
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7977 - val_loss: 1.0693
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7931 - val_loss: 1.0539
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7840 - val_loss: 1.0467
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8044 - val_loss: 1.0275
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7948 - val_loss: 1.0164
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7730 - val_loss: 0.9996
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7696 - val_loss: 1.0434
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7742 - val_loss: 1.0468
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7690 - val_loss: 1.0250
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7591 - val_loss: 1.0077
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7522 - val_loss: 1.0035
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7539 - val_loss: 1.0648
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7495 - val_loss: 1.0087
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7446 - val_loss: 0.9807
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7593 - val_loss: 1.0182
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7539 - val_loss: 1.0105
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7423 - val_loss: 0.9900
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7330 - val_loss: 0.9784
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7353 - val_loss: 1.0167
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7335 - val_loss: 0.9813
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7414 - val_loss: 1.0024
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7246 - val_loss: 0.9741
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7263 - val_loss: 0.9648
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7317 - val_loss: 0.9691
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7189 - val_loss: 1.0211
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7321 - val_loss: 0.9782
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7208 - val_loss: 1.0037
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7230 - val_loss: 0.9821
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7155 - val_loss: 0.9749
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7194 - val_loss: 0.9368
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7161 - val_loss: 1.0033
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7177 - val_loss: 0.9939
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7235 - val_loss: 0.9452
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7066 - val_loss: 0.9711
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7140 - val_loss: 0.9493
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7071 - val_loss: 0.9902
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7138 - val_loss: 0.9635
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7008 - val_loss: 0.9409
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7215 - val_loss: 0.9378
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7062 - val_loss: 0.9548
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7023 - val_loss: 0.9307
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7103 - val_loss: 0.9414
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6994 - val_loss: 0.9610
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6984 - val_loss: 0.9498
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7014 - val_loss: 0.9526
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7065 - val_loss: 0.9600
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7175 - val_loss: 0.9635
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6936 - val_loss: 0.9759
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6992 - val_loss: 0.9611
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6946 - val_loss: 0.9658
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6890 - val_loss: 0.9404
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6943 - val_loss: 0.9563
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6979 - val_loss: 1.0018
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7048 - val_loss: 0.9274
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6938 - val_loss: 0.9481
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6978 - val_loss: 0.9535
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7008 - val_loss: 0.9610
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6874 - val_loss: 0.9141
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6982 - val_loss: 0.9512
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6894 - val_loss: 0.9392
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6926 - val_loss: 0.9485
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6871 - val_loss: 0.9516
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6967 - val_loss: 0.9716
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6758 - val_loss: 0.9454
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6844 - val_loss: 0.9528
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6957 - val_loss: 0.9574
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6902 - val_loss: 0.9334
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6882 - val_loss: 0.9358
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6988 - val_loss: 0.9537
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6869 - val_loss: 0.9468
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6808 - val_loss: 0.9413
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6819 - val_loss: 0.9698
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6783 - val_loss: 0.9338
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6846 - val_loss: 0.9445
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6845 - val_loss: 0.9186
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6772 - val_loss: 0.9234
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6750 - val_loss: 0.9241
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6755 - val_loss: 0.9234
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6869 - val_loss: 0.9232
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6824 - val_loss: 0.9159
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6763 - val_loss: 0.9188
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6800 - val_loss: 0.9400
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6724 - val_loss: 0.9226
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6736 - val_loss: 0.9555
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6732 - val_loss: 0.9353
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6742 - val_loss: 0.9617
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6750 - val_loss: 0.9270
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6727 - val_loss: 0.9117
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_35 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_110 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_110 ( [()]                 0           tf_op_layer_Shape_110[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_11 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_110[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_11[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_33 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_11 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_33[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_111 (TensorFl [(2,)]               0           tf_op_layer_Add_11[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_113 (TensorFl [(2,)]               0           tf_op_layer_Add_11[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_112 (TensorFl [(2,)]               0           tf_op_layer_Add_11[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_114 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_116 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_115 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_117 (TensorFl [(2,)]               0           tf_op_layer_Add_11[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_119 (TensorFl [(2,)]               0           tf_op_layer_Add_11[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_118 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_111 ( [()]                 0           tf_op_layer_Shape_111[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_113 ( [()]                 0           tf_op_layer_Shape_113[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_112 ( [()]                 0           tf_op_layer_Shape_112[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_114 ( [()]                 0           tf_op_layer_Shape_114[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_116 ( [()]                 0           tf_op_layer_Shape_116[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_115 ( [()]                 0           tf_op_layer_Shape_115[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_117 ( [()]                 0           tf_op_layer_Shape_117[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_119 ( [()]                 0           tf_op_layer_Shape_119[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_118 ( [()]                 0           tf_op_layer_Shape_118[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_66/shape (T [(3,)]               0           tf_op_layer_strided_slice_111[0][
                                                                 tf_op_layer_strided_slice_113[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_67/shape (T [(3,)]               0           tf_op_layer_strided_slice_112[0][
                                                                 tf_op_layer_strided_slice_113[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_68/shape (T [(3,)]               0           tf_op_layer_strided_slice_114[0][
                                                                 tf_op_layer_strided_slice_116[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_69/shape (T [(3,)]               0           tf_op_layer_strided_slice_115[0][
                                                                 tf_op_layer_strided_slice_116[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_70/shape (T [(3,)]               0           tf_op_layer_strided_slice_117[0][
                                                                 tf_op_layer_strided_slice_119[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_71/shape (T [(3,)]               0           tf_op_layer_strided_slice_118[0][
                                                                 tf_op_layer_strided_slice_119[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_66 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_11[0][0]         
                                                                 tf_op_layer_Reshape_66/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_66/multiples ( [(3,)]               0           tf_op_layer_strided_slice_112[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_67 (TensorF [(1, None, None)]    0           tf_op_layer_Add_11[0][0]         
                                                                 tf_op_layer_Reshape_67/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_67/multiples ( [(3,)]               0           tf_op_layer_strided_slice_111[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_68 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_68/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_68/multiples ( [(3,)]               0           tf_op_layer_strided_slice_115[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_69 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_69/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_69/multiples ( [(3,)]               0           tf_op_layer_strided_slice_114[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_70 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_11[0][0]         
                                                                 tf_op_layer_Reshape_70/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_70/multiples ( [(3,)]               0           tf_op_layer_strided_slice_118[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_71 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_71/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_71/multiples ( [(3,)]               0           tf_op_layer_strided_slice_117[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_66 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_66[0][0]     
                                                                 tf_op_layer_Tile_66/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_67 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_67[0][0]     
                                                                 tf_op_layer_Tile_67/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_68 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_68[0][0]     
                                                                 tf_op_layer_Tile_68/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_69 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_69[0][0]     
                                                                 tf_op_layer_Tile_69/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_70 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_70[0][0]     
                                                                 tf_op_layer_Tile_70/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_71 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_71[0][0]     
                                                                 tf_op_layer_Tile_71/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_55 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_66[0][0]        
                                                                 tf_op_layer_Tile_67[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_56 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_68[0][0]        
                                                                 tf_op_layer_Tile_69[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_57 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_70[0][0]        
                                                                 tf_op_layer_Tile_71[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_44 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_55[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_45 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_56[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_46 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_57[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_77 (TensorFlow [(None, None)]       0           tf_op_layer_Square_44[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_78 (TensorFlow [(None, None)]       0           tf_op_layer_Square_45[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_79 (TensorFlow [(None, None)]       0           tf_op_layer_Square_46[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_33 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_77[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_33 (TensorFlow [()]                 0           tf_op_layer_strided_slice_113[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_34 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_78[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_34 (TensorFlow [()]                 0           tf_op_layer_strided_slice_116[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_35 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_79[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_35 (TensorFlow [()]                 0           tf_op_layer_strided_slice_119[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_33 (TensorF [(None, None)]       0           tf_op_layer_Neg_33[0][0]         
                                                                 tf_op_layer_Cast_33[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_34 (TensorF [(None, None)]       0           tf_op_layer_Neg_34[0][0]         
                                                                 tf_op_layer_Cast_34[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_35 (TensorF [(None, None)]       0           tf_op_layer_Neg_35[0][0]         
                                                                 tf_op_layer_Cast_35[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_33 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_33[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_34 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_34[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_35 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_35[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_80 (TensorFlow [()]                 0           tf_op_layer_Exp_33[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_81 (TensorFlow [()]                 0           tf_op_layer_Exp_34[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_82 (TensorFlow [()]                 0           tf_op_layer_Exp_35[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_59 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_22 (TensorFlo [()]                 0           tf_op_layer_Mean_80[0][0]        
                                                                 tf_op_layer_Mean_81[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_34 (TensorFlowO [()]                 0           tf_op_layer_Mean_82[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_47 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_59[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_58 (TensorFlowO [()]                 0           tf_op_layer_AddV2_22[0][0]       
                                                                 tf_op_layer_Mul_34[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_11 (TensorFlowO [(None,)]            0           tf_op_layer_Square_47[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_35 (TensorFlowO [()]                 0           tf_op_layer_Sub_58[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_23 (TensorFlo [(None,)]            0           tf_op_layer_Sum_11[0][0]         
                                                                 tf_op_layer_Mul_35[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_83 (TensorFlow [()]                 0           tf_op_layer_AddV2_23[0][0]       
__________________________________________________________________________________________________
add_loss_11 (AddLoss)           ()                   0           tf_op_layer_Mean_83[0][0]        
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.7008WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.1407s). Check your callbacks.
24/24 [==============================] - 0s 16ms/step - loss: 8.1315 - val_loss: 5.6715
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.3263 - val_loss: 3.5628
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.6452 - val_loss: 2.4404
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.9991 - val_loss: 2.1430
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7961 - val_loss: 2.0194
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6599 - val_loss: 1.9324
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5624 - val_loss: 1.8310
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4828 - val_loss: 1.7218
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4150 - val_loss: 1.6684
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3421 - val_loss: 1.6559
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3009 - val_loss: 1.6126
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2588 - val_loss: 1.5212
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2458 - val_loss: 1.5493
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2210 - val_loss: 1.5346
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1837 - val_loss: 1.5162
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1749 - val_loss: 1.4373
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1522 - val_loss: 1.4280
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1340 - val_loss: 1.4828
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1363 - val_loss: 1.4714
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1172 - val_loss: 1.4178
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1113 - val_loss: 1.4180
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0986 - val_loss: 1.4537
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1192 - val_loss: 1.4723
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0919 - val_loss: 1.4158
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1059 - val_loss: 1.3881
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0761 - val_loss: 1.3939
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0685 - val_loss: 1.3770
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0593 - val_loss: 1.4140
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0800 - val_loss: 1.4100
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0637 - val_loss: 1.3859
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0626 - val_loss: 1.4058
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0459 - val_loss: 1.3579
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0594 - val_loss: 1.3893
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0585 - val_loss: 1.3736
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0300 - val_loss: 1.4194
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0441 - val_loss: 1.3624
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0449 - val_loss: 1.4241
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0269 - val_loss: 1.3199
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0496 - val_loss: 1.3546
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0383 - val_loss: 1.3163
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0151 - val_loss: 1.3775
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0279 - val_loss: 1.3523
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0414 - val_loss: 1.3454
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0148 - val_loss: 1.3384
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0197 - val_loss: 1.3525
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0169 - val_loss: 1.3307
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0239 - val_loss: 1.3362
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0108 - val_loss: 1.3200
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0224 - val_loss: 1.3061
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0157 - val_loss: 1.3108
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0059 - val_loss: 1.3337
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0237 - val_loss: 1.3335
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0100 - val_loss: 1.3221
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0142 - val_loss: 1.3314
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0016 - val_loss: 1.3286
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0078 - val_loss: 1.2811
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0070 - val_loss: 1.3305
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0018 - val_loss: 1.3474
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0039 - val_loss: 1.3223
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0052 - val_loss: 1.3168
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9943 - val_loss: 1.3065
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9858 - val_loss: 1.2746
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9998 - val_loss: 1.3237
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9950 - val_loss: 1.3442
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9909 - val_loss: 1.3253
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9955 - val_loss: 1.3085
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9962 - val_loss: 1.3014
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9970 - val_loss: 1.2655
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9860 - val_loss: 1.2977
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9896 - val_loss: 1.3504
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9890 - val_loss: 1.2915
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9734 - val_loss: 1.2874
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9804 - val_loss: 1.2986
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9703 - val_loss: 1.3076
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9821 - val_loss: 1.3097
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9827 - val_loss: 1.2917
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9770 - val_loss: 1.3021
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9713 - val_loss: 1.2865
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9837 - val_loss: 1.2877
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9816 - val_loss: 1.3190
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9858 - val_loss: 1.2999
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9789 - val_loss: 1.3256
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9826 - val_loss: 1.2685
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9787 - val_loss: 1.2775
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9696 - val_loss: 1.2960
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9766 - val_loss: 1.2761
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9692 - val_loss: 1.2793
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9900 - val_loss: 1.2456
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9683 - val_loss: 1.2983
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9717 - val_loss: 1.2575
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9718 - val_loss: 1.2811
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9618 - val_loss: 1.2873
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9778 - val_loss: 1.2669
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9659 - val_loss: 1.2873
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9591 - val_loss: 1.3286
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9767 - val_loss: 1.2835
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9549 - val_loss: 1.2819
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9665 - val_loss: 1.2680
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9744 - val_loss: 1.2812
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9638 - val_loss: 1.2680
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.33 seconds.
    Calculating affinities...
    Calculated affinities in 0.03 seconds.
  Calculated graph and diffusion operator in 0.38 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.68 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.23 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 8.57 seconds.
Calculated PHATE in 10.88 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_36 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_37 (Dense)                (None, 32)           2080        dense_36[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_37[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_37[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_38 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_39 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_40 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_36 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_37 (Dense)                (None, 32)           2080        dense_36[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_37[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_37[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_120 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_120 ( [()]                 0           tf_op_layer_Shape_120[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_12 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_120[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_12[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_36 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_12 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_36[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_121 (TensorFl [(2,)]               0           tf_op_layer_Add_12[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_123 (TensorFl [(2,)]               0           tf_op_layer_Add_12[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_122 (TensorFl [(2,)]               0           tf_op_layer_Add_12[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_124 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_126 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_125 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_127 (TensorFl [(2,)]               0           tf_op_layer_Add_12[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_129 (TensorFl [(2,)]               0           tf_op_layer_Add_12[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_128 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_121 ( [()]                 0           tf_op_layer_Shape_121[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_123 ( [()]                 0           tf_op_layer_Shape_123[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_122 ( [()]                 0           tf_op_layer_Shape_122[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_124 ( [()]                 0           tf_op_layer_Shape_124[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_126 ( [()]                 0           tf_op_layer_Shape_126[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_125 ( [()]                 0           tf_op_layer_Shape_125[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_127 ( [()]                 0           tf_op_layer_Shape_127[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_129 ( [()]                 0           tf_op_layer_Shape_129[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_128 ( [()]                 0           tf_op_layer_Shape_128[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_72/shape (T [(3,)]               0           tf_op_layer_strided_slice_121[0][
                                                                 tf_op_layer_strided_slice_123[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_73/shape (T [(3,)]               0           tf_op_layer_strided_slice_122[0][
                                                                 tf_op_layer_strided_slice_123[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_74/shape (T [(3,)]               0           tf_op_layer_strided_slice_124[0][
                                                                 tf_op_layer_strided_slice_126[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_75/shape (T [(3,)]               0           tf_op_layer_strided_slice_125[0][
                                                                 tf_op_layer_strided_slice_126[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_76/shape (T [(3,)]               0           tf_op_layer_strided_slice_127[0][
                                                                 tf_op_layer_strided_slice_129[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_77/shape (T [(3,)]               0           tf_op_layer_strided_slice_128[0][
                                                                 tf_op_layer_strided_slice_129[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_72 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_12[0][0]         
                                                                 tf_op_layer_Reshape_72/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_72/multiples ( [(3,)]               0           tf_op_layer_strided_slice_122[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_73 (TensorF [(1, None, None)]    0           tf_op_layer_Add_12[0][0]         
                                                                 tf_op_layer_Reshape_73/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_73/multiples ( [(3,)]               0           tf_op_layer_strided_slice_121[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_74 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_74/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_74/multiples ( [(3,)]               0           tf_op_layer_strided_slice_125[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_75 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_75/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_75/multiples ( [(3,)]               0           tf_op_layer_strided_slice_124[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_76 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_12[0][0]         
                                                                 tf_op_layer_Reshape_76/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_76/multiples ( [(3,)]               0           tf_op_layer_strided_slice_128[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_77 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_77/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_77/multiples ( [(3,)]               0           tf_op_layer_strided_slice_127[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_72 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_72[0][0]     
                                                                 tf_op_layer_Tile_72/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_73 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_73[0][0]     
                                                                 tf_op_layer_Tile_73/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_74 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_74[0][0]     
                                                                 tf_op_layer_Tile_74/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_75 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_75[0][0]     
                                                                 tf_op_layer_Tile_75/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_76 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_76[0][0]     
                                                                 tf_op_layer_Tile_76/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_77 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_77[0][0]     
                                                                 tf_op_layer_Tile_77/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_60 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_72[0][0]        
                                                                 tf_op_layer_Tile_73[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_61 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_74[0][0]        
                                                                 tf_op_layer_Tile_75[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_62 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_76[0][0]        
                                                                 tf_op_layer_Tile_77[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_48 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_60[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_49 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_61[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_50 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_62[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_84 (TensorFlow [(None, None)]       0           tf_op_layer_Square_48[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_85 (TensorFlow [(None, None)]       0           tf_op_layer_Square_49[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_86 (TensorFlow [(None, None)]       0           tf_op_layer_Square_50[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_36 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_84[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_36 (TensorFlow [()]                 0           tf_op_layer_strided_slice_123[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_37 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_85[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_37 (TensorFlow [()]                 0           tf_op_layer_strided_slice_126[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_38 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_86[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_38 (TensorFlow [()]                 0           tf_op_layer_strided_slice_129[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_36 (TensorF [(None, None)]       0           tf_op_layer_Neg_36[0][0]         
                                                                 tf_op_layer_Cast_36[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_37 (TensorF [(None, None)]       0           tf_op_layer_Neg_37[0][0]         
                                                                 tf_op_layer_Cast_37[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_38 (TensorF [(None, None)]       0           tf_op_layer_Neg_38[0][0]         
                                                                 tf_op_layer_Cast_38[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_36 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_36[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_37 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_37[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_38 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_38[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_87 (TensorFlow [()]                 0           tf_op_layer_Exp_36[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_88 (TensorFlow [()]                 0           tf_op_layer_Exp_37[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_89 (TensorFlow [()]                 0           tf_op_layer_Exp_38[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_64 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_24 (TensorFlo [()]                 0           tf_op_layer_Mean_87[0][0]        
                                                                 tf_op_layer_Mean_88[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_37 (TensorFlowO [()]                 0           tf_op_layer_Mean_89[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_51 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_64[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_63 (TensorFlowO [()]                 0           tf_op_layer_AddV2_24[0][0]       
                                                                 tf_op_layer_Mul_37[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_12 (TensorFlowO [(None,)]            0           tf_op_layer_Square_51[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_38 (TensorFlowO [()]                 0           tf_op_layer_Sub_63[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_25 (TensorFlo [(None,)]            0           tf_op_layer_Sum_12[0][0]         
                                                                 tf_op_layer_Mul_38[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_90 (TensorFlow [()]                 0           tf_op_layer_AddV2_25[0][0]       
__________________________________________________________________________________________________
add_loss_12 (AddLoss)           ()                   0           tf_op_layer_Mean_90[0][0]        
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.6709 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.1623s). Check your callbacks.
24/24 [==============================] - 0s 20ms/step - loss: 5.2819 - val_loss: 2.7194
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.7670 - val_loss: 1.3248
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2756 - val_loss: 1.2405
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2006 - val_loss: 1.1695
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1432 - val_loss: 1.1203
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0950 - val_loss: 1.1531
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0528 - val_loss: 1.1099
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9772 - val_loss: 0.9695
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8978 - val_loss: 0.9177
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8802 - val_loss: 0.9208
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8525 - val_loss: 0.9373
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8512 - val_loss: 0.9299
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8566 - val_loss: 0.9051
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8254 - val_loss: 0.8866
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8228 - val_loss: 0.9271
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8182 - val_loss: 0.8589
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8080 - val_loss: 0.8733
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8043 - val_loss: 0.8452
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8098 - val_loss: 0.8564
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8028 - val_loss: 0.8656
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8002 - val_loss: 0.8387
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7923 - val_loss: 0.8658
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7834 - val_loss: 0.8728
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7750 - val_loss: 0.8693
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7820 - val_loss: 0.8152
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7759 - val_loss: 0.8466
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7654 - val_loss: 0.8250
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7657 - val_loss: 0.8547
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7734 - val_loss: 0.7972
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7551 - val_loss: 0.8092
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7575 - val_loss: 0.8201
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7797 - val_loss: 0.8480
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7510 - val_loss: 0.8443
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7519 - val_loss: 0.8173
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7527 - val_loss: 0.8559
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7509 - val_loss: 0.8245
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7461 - val_loss: 0.8191
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7503 - val_loss: 0.7799
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7433 - val_loss: 0.8403
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7382 - val_loss: 0.8170
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7468 - val_loss: 0.8340
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7352 - val_loss: 0.8127
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7408 - val_loss: 0.8548
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7357 - val_loss: 0.7874
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7324 - val_loss: 0.8425
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7328 - val_loss: 0.8164
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7387 - val_loss: 0.8080
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7279 - val_loss: 0.8051
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7340 - val_loss: 0.8107
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7305 - val_loss: 0.7945
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7420 - val_loss: 0.7957
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7310 - val_loss: 0.8566
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7361 - val_loss: 0.8096
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7263 - val_loss: 0.8162
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7292 - val_loss: 0.8130
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7238 - val_loss: 0.8468
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7233 - val_loss: 0.8533
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7256 - val_loss: 0.7811
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7219 - val_loss: 0.8309
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7215 - val_loss: 0.7610
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7169 - val_loss: 0.8048
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7296 - val_loss: 0.8437
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7303 - val_loss: 0.8255
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7189 - val_loss: 0.8267
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7139 - val_loss: 0.8283
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7139 - val_loss: 0.7918
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7260 - val_loss: 0.7932
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7104 - val_loss: 0.8036
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7105 - val_loss: 0.8338
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7031 - val_loss: 0.7969
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7149 - val_loss: 0.7886
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7029 - val_loss: 0.7904
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7085 - val_loss: 0.7588
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7014 - val_loss: 0.8197
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7057 - val_loss: 0.8024
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7028 - val_loss: 0.7539
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7027 - val_loss: 0.7930
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6949 - val_loss: 0.7581
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6995 - val_loss: 0.8017
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6958 - val_loss: 0.7815
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7001 - val_loss: 0.7877
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7040 - val_loss: 0.7764
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6952 - val_loss: 0.7757
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7115 - val_loss: 0.7567
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6958 - val_loss: 0.8639
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6895 - val_loss: 0.7683
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6972 - val_loss: 0.7572
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6894 - val_loss: 0.7635
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6894 - val_loss: 0.7647
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6928 - val_loss: 0.7989
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6930 - val_loss: 0.7803
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6921 - val_loss: 0.7773
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6837 - val_loss: 0.7971
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6862 - val_loss: 0.7705
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6910 - val_loss: 0.7781
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6941 - val_loss: 0.7673
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6931 - val_loss: 0.7670
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6937 - val_loss: 0.7533
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6936 - val_loss: 0.7595
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6831 - val_loss: 0.7554
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_41 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_130 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_130 ( [()]                 0           tf_op_layer_Shape_130[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_13 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_130[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_13[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_39 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_13 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_39[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_131 (TensorFl [(2,)]               0           tf_op_layer_Add_13[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_133 (TensorFl [(2,)]               0           tf_op_layer_Add_13[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_132 (TensorFl [(2,)]               0           tf_op_layer_Add_13[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_134 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_136 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_135 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_137 (TensorFl [(2,)]               0           tf_op_layer_Add_13[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_139 (TensorFl [(2,)]               0           tf_op_layer_Add_13[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_138 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_131 ( [()]                 0           tf_op_layer_Shape_131[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_133 ( [()]                 0           tf_op_layer_Shape_133[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_132 ( [()]                 0           tf_op_layer_Shape_132[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_134 ( [()]                 0           tf_op_layer_Shape_134[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_136 ( [()]                 0           tf_op_layer_Shape_136[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_135 ( [()]                 0           tf_op_layer_Shape_135[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_137 ( [()]                 0           tf_op_layer_Shape_137[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_139 ( [()]                 0           tf_op_layer_Shape_139[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_138 ( [()]                 0           tf_op_layer_Shape_138[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_78/shape (T [(3,)]               0           tf_op_layer_strided_slice_131[0][
                                                                 tf_op_layer_strided_slice_133[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_79/shape (T [(3,)]               0           tf_op_layer_strided_slice_132[0][
                                                                 tf_op_layer_strided_slice_133[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_80/shape (T [(3,)]               0           tf_op_layer_strided_slice_134[0][
                                                                 tf_op_layer_strided_slice_136[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_81/shape (T [(3,)]               0           tf_op_layer_strided_slice_135[0][
                                                                 tf_op_layer_strided_slice_136[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_82/shape (T [(3,)]               0           tf_op_layer_strided_slice_137[0][
                                                                 tf_op_layer_strided_slice_139[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_83/shape (T [(3,)]               0           tf_op_layer_strided_slice_138[0][
                                                                 tf_op_layer_strided_slice_139[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_78 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_13[0][0]         
                                                                 tf_op_layer_Reshape_78/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_78/multiples ( [(3,)]               0           tf_op_layer_strided_slice_132[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_79 (TensorF [(1, None, None)]    0           tf_op_layer_Add_13[0][0]         
                                                                 tf_op_layer_Reshape_79/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_79/multiples ( [(3,)]               0           tf_op_layer_strided_slice_131[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_80 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_80/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_80/multiples ( [(3,)]               0           tf_op_layer_strided_slice_135[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_81 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_81/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_81/multiples ( [(3,)]               0           tf_op_layer_strided_slice_134[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_82 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_13[0][0]         
                                                                 tf_op_layer_Reshape_82/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_82/multiples ( [(3,)]               0           tf_op_layer_strided_slice_138[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_83 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_83/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_83/multiples ( [(3,)]               0           tf_op_layer_strided_slice_137[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_78 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_78[0][0]     
                                                                 tf_op_layer_Tile_78/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_79 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_79[0][0]     
                                                                 tf_op_layer_Tile_79/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_80 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_80[0][0]     
                                                                 tf_op_layer_Tile_80/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_81 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_81[0][0]     
                                                                 tf_op_layer_Tile_81/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_82 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_82[0][0]     
                                                                 tf_op_layer_Tile_82/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_83 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_83[0][0]     
                                                                 tf_op_layer_Tile_83/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_65 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_78[0][0]        
                                                                 tf_op_layer_Tile_79[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_66 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_80[0][0]        
                                                                 tf_op_layer_Tile_81[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_67 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_82[0][0]        
                                                                 tf_op_layer_Tile_83[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_52 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_65[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_53 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_66[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_54 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_67[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_91 (TensorFlow [(None, None)]       0           tf_op_layer_Square_52[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_92 (TensorFlow [(None, None)]       0           tf_op_layer_Square_53[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_93 (TensorFlow [(None, None)]       0           tf_op_layer_Square_54[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_39 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_91[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_39 (TensorFlow [()]                 0           tf_op_layer_strided_slice_133[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_40 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_92[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_40 (TensorFlow [()]                 0           tf_op_layer_strided_slice_136[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_41 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_93[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_41 (TensorFlow [()]                 0           tf_op_layer_strided_slice_139[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_39 (TensorF [(None, None)]       0           tf_op_layer_Neg_39[0][0]         
                                                                 tf_op_layer_Cast_39[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_40 (TensorF [(None, None)]       0           tf_op_layer_Neg_40[0][0]         
                                                                 tf_op_layer_Cast_40[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_41 (TensorF [(None, None)]       0           tf_op_layer_Neg_41[0][0]         
                                                                 tf_op_layer_Cast_41[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_39 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_39[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_40 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_40[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_41 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_41[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_94 (TensorFlow [()]                 0           tf_op_layer_Exp_39[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_95 (TensorFlow [()]                 0           tf_op_layer_Exp_40[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_96 (TensorFlow [()]                 0           tf_op_layer_Exp_41[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_69 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_26 (TensorFlo [()]                 0           tf_op_layer_Mean_94[0][0]        
                                                                 tf_op_layer_Mean_95[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_40 (TensorFlowO [()]                 0           tf_op_layer_Mean_96[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_55 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_69[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_68 (TensorFlowO [()]                 0           tf_op_layer_AddV2_26[0][0]       
                                                                 tf_op_layer_Mul_40[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_13 (TensorFlowO [(None,)]            0           tf_op_layer_Square_55[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_41 (TensorFlowO [()]                 0           tf_op_layer_Sub_68[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_27 (TensorFlo [(None,)]            0           tf_op_layer_Sum_13[0][0]         
                                                                 tf_op_layer_Mul_41[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_97 (TensorFlow [()]                 0           tf_op_layer_AddV2_27[0][0]       
__________________________________________________________________________________________________
add_loss_13 (AddLoss)           ()                   0           tf_op_layer_Mean_97[0][0]        
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.2700WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1514s). Check your callbacks.
24/24 [==============================] - 0s 17ms/step - loss: 7.8993 - val_loss: 5.8785
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.1433 - val_loss: 3.0783
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.5709 - val_loss: 1.9757
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.0369 - val_loss: 1.7072
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8389 - val_loss: 1.5985
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7018 - val_loss: 1.4891
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6175 - val_loss: 1.4444
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5136 - val_loss: 1.3962
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4714 - val_loss: 1.3127
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3954 - val_loss: 1.3028
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3340 - val_loss: 1.2660
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3003 - val_loss: 1.2397
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2696 - val_loss: 1.1848
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2457 - val_loss: 1.1853
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2305 - val_loss: 1.1659
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2015 - val_loss: 1.1545
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1997 - val_loss: 1.1218
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1736 - val_loss: 1.1228
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1725 - val_loss: 1.0740
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1560 - val_loss: 1.0998
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1497 - val_loss: 1.1129
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1639 - val_loss: 1.0915
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1457 - val_loss: 1.0842
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1307 - val_loss: 1.1140
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1319 - val_loss: 1.0579
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1156 - val_loss: 1.0871
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1045 - val_loss: 1.0835
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1201 - val_loss: 1.0772
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1119 - val_loss: 1.0450
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1008 - val_loss: 1.0338
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0939 - val_loss: 1.0341
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0960 - val_loss: 1.0392
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0754 - val_loss: 1.0349
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0735 - val_loss: 1.0252
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0911 - val_loss: 1.0265
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0757 - val_loss: 1.0370
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0821 - val_loss: 1.0062
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0863 - val_loss: 1.0076
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0722 - val_loss: 1.0293
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0607 - val_loss: 0.9942
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0533 - val_loss: 0.9728
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0735 - val_loss: 1.0132
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0644 - val_loss: 0.9972
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0491 - val_loss: 0.9820
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0550 - val_loss: 0.9755
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0539 - val_loss: 1.0068
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0360 - val_loss: 0.9878
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0567 - val_loss: 0.9909
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0397 - val_loss: 0.9595
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0292 - val_loss: 0.9699
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0514 - val_loss: 0.9822
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0410 - val_loss: 0.9705
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0499 - val_loss: 0.9829
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0302 - val_loss: 0.9802
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0361 - val_loss: 0.9895
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0322 - val_loss: 0.9633
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0195 - val_loss: 0.9927
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0256 - val_loss: 0.9661
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0221 - val_loss: 0.9622
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0250 - val_loss: 0.9838
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0292 - val_loss: 0.9646
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0334 - val_loss: 0.9869
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0277 - val_loss: 0.9933
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0114 - val_loss: 0.9655
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0219 - val_loss: 0.9808
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0290 - val_loss: 0.9619
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0294 - val_loss: 0.9932
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0183 - val_loss: 0.9730
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0430 - val_loss: 0.9596
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0051 - val_loss: 0.9663
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0131 - val_loss: 0.9887
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0201 - val_loss: 0.9622
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0191 - val_loss: 0.9639
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0180 - val_loss: 0.9621
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0065 - val_loss: 0.9484
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0029 - val_loss: 0.9514
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0079 - val_loss: 0.9676
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0164 - val_loss: 0.9317
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0084 - val_loss: 0.9478
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0129 - val_loss: 0.9393
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0097 - val_loss: 0.9435
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9899 - val_loss: 0.9794
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9970 - val_loss: 0.9987
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0053 - val_loss: 0.9534
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0027 - val_loss: 0.9314
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0173 - val_loss: 0.9587
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9982 - val_loss: 0.9372
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9952 - val_loss: 0.9534
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0193 - val_loss: 0.9489
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0041 - val_loss: 0.9450
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0069 - val_loss: 0.9687
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0055 - val_loss: 0.9287
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0099 - val_loss: 0.9380
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0050 - val_loss: 0.9685
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9929 - val_loss: 0.9476
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9913 - val_loss: 0.9493
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9835 - val_loss: 0.9574
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0048 - val_loss: 0.9578
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9877 - val_loss: 0.9191
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9980 - val_loss: 0.9367
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.27 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.29 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.19 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.21 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 7.89 seconds.
Calculated PHATE in 9.60 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_42 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_43 (Dense)                (None, 32)           2080        dense_42[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_43[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_43[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_44 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_45 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_46 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_42 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_43 (Dense)                (None, 32)           2080        dense_42[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_43[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_43[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_140 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_140 ( [()]                 0           tf_op_layer_Shape_140[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_14 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_140[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_14[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_42 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_14 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_42[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_141 (TensorFl [(2,)]               0           tf_op_layer_Add_14[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_143 (TensorFl [(2,)]               0           tf_op_layer_Add_14[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_142 (TensorFl [(2,)]               0           tf_op_layer_Add_14[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_144 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_146 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_145 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_147 (TensorFl [(2,)]               0           tf_op_layer_Add_14[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_149 (TensorFl [(2,)]               0           tf_op_layer_Add_14[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_148 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_141 ( [()]                 0           tf_op_layer_Shape_141[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_143 ( [()]                 0           tf_op_layer_Shape_143[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_142 ( [()]                 0           tf_op_layer_Shape_142[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_144 ( [()]                 0           tf_op_layer_Shape_144[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_146 ( [()]                 0           tf_op_layer_Shape_146[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_145 ( [()]                 0           tf_op_layer_Shape_145[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_147 ( [()]                 0           tf_op_layer_Shape_147[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_149 ( [()]                 0           tf_op_layer_Shape_149[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_148 ( [()]                 0           tf_op_layer_Shape_148[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_84/shape (T [(3,)]               0           tf_op_layer_strided_slice_141[0][
                                                                 tf_op_layer_strided_slice_143[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_85/shape (T [(3,)]               0           tf_op_layer_strided_slice_142[0][
                                                                 tf_op_layer_strided_slice_143[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_86/shape (T [(3,)]               0           tf_op_layer_strided_slice_144[0][
                                                                 tf_op_layer_strided_slice_146[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_87/shape (T [(3,)]               0           tf_op_layer_strided_slice_145[0][
                                                                 tf_op_layer_strided_slice_146[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_88/shape (T [(3,)]               0           tf_op_layer_strided_slice_147[0][
                                                                 tf_op_layer_strided_slice_149[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_89/shape (T [(3,)]               0           tf_op_layer_strided_slice_148[0][
                                                                 tf_op_layer_strided_slice_149[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_84 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_14[0][0]         
                                                                 tf_op_layer_Reshape_84/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_84/multiples ( [(3,)]               0           tf_op_layer_strided_slice_142[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_85 (TensorF [(1, None, None)]    0           tf_op_layer_Add_14[0][0]         
                                                                 tf_op_layer_Reshape_85/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_85/multiples ( [(3,)]               0           tf_op_layer_strided_slice_141[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_86 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_86/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_86/multiples ( [(3,)]               0           tf_op_layer_strided_slice_145[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_87 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_87/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_87/multiples ( [(3,)]               0           tf_op_layer_strided_slice_144[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_88 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_14[0][0]         
                                                                 tf_op_layer_Reshape_88/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_88/multiples ( [(3,)]               0           tf_op_layer_strided_slice_148[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_89 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_89/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_89/multiples ( [(3,)]               0           tf_op_layer_strided_slice_147[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_84 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_84[0][0]     
                                                                 tf_op_layer_Tile_84/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_85 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_85[0][0]     
                                                                 tf_op_layer_Tile_85/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_86 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_86[0][0]     
                                                                 tf_op_layer_Tile_86/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_87 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_87[0][0]     
                                                                 tf_op_layer_Tile_87/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_88 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_88[0][0]     
                                                                 tf_op_layer_Tile_88/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_89 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_89[0][0]     
                                                                 tf_op_layer_Tile_89/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_70 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_84[0][0]        
                                                                 tf_op_layer_Tile_85[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_71 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_86[0][0]        
                                                                 tf_op_layer_Tile_87[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_72 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_88[0][0]        
                                                                 tf_op_layer_Tile_89[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_56 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_70[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_57 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_71[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_58 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_72[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_98 (TensorFlow [(None, None)]       0           tf_op_layer_Square_56[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_99 (TensorFlow [(None, None)]       0           tf_op_layer_Square_57[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_100 (TensorFlo [(None, None)]       0           tf_op_layer_Square_58[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_42 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_98[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_42 (TensorFlow [()]                 0           tf_op_layer_strided_slice_143[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_43 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_99[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Cast_43 (TensorFlow [()]                 0           tf_op_layer_strided_slice_146[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_44 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_100[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_44 (TensorFlow [()]                 0           tf_op_layer_strided_slice_149[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_42 (TensorF [(None, None)]       0           tf_op_layer_Neg_42[0][0]         
                                                                 tf_op_layer_Cast_42[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_43 (TensorF [(None, None)]       0           tf_op_layer_Neg_43[0][0]         
                                                                 tf_op_layer_Cast_43[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_44 (TensorF [(None, None)]       0           tf_op_layer_Neg_44[0][0]         
                                                                 tf_op_layer_Cast_44[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_42 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_42[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_43 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_43[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_44 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_44[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_101 (TensorFlo [()]                 0           tf_op_layer_Exp_42[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_102 (TensorFlo [()]                 0           tf_op_layer_Exp_43[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_103 (TensorFlo [()]                 0           tf_op_layer_Exp_44[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_74 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_28 (TensorFlo [()]                 0           tf_op_layer_Mean_101[0][0]       
                                                                 tf_op_layer_Mean_102[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_43 (TensorFlowO [()]                 0           tf_op_layer_Mean_103[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_59 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_74[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_73 (TensorFlowO [()]                 0           tf_op_layer_AddV2_28[0][0]       
                                                                 tf_op_layer_Mul_43[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_14 (TensorFlowO [(None,)]            0           tf_op_layer_Square_59[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_44 (TensorFlowO [()]                 0           tf_op_layer_Sub_73[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_29 (TensorFlo [(None,)]            0           tf_op_layer_Sum_14[0][0]         
                                                                 tf_op_layer_Mul_44[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_104 (TensorFlo [()]                 0           tf_op_layer_AddV2_29[0][0]       
__________________________________________________________________________________________________
add_loss_14 (AddLoss)           ()                   0           tf_op_layer_Mean_104[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.6812 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0021s vs `on_train_batch_end` time: 0.1595s). Check your callbacks.
24/24 [==============================] - 1s 23ms/step - loss: 4.8895 - val_loss: 2.5642
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.7862 - val_loss: 1.6656
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2895 - val_loss: 1.5012
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2185 - val_loss: 1.4718
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1000 - val_loss: 1.2997
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0201 - val_loss: 1.1235
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9424 - val_loss: 0.9759
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8929 - val_loss: 0.9154
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8635 - val_loss: 0.9142
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8631 - val_loss: 0.9075
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8392 - val_loss: 0.9161
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8200 - val_loss: 0.9403
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8199 - val_loss: 0.8702
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8031 - val_loss: 0.8759
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8025 - val_loss: 0.8610
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8072 - val_loss: 0.8466
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7925 - val_loss: 0.8379
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7966 - val_loss: 0.8461
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7788 - val_loss: 0.8283
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7781 - val_loss: 0.8524
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7878 - val_loss: 0.8547
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7731 - val_loss: 0.8191
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7808 - val_loss: 0.8593
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7597 - val_loss: 0.8212
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7643 - val_loss: 0.8410
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7754 - val_loss: 0.8563
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7572 - val_loss: 0.8195
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7482 - val_loss: 0.8386
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7485 - val_loss: 0.8098
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7516 - val_loss: 0.8400
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7424 - val_loss: 0.8225
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7409 - val_loss: 0.8053
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7469 - val_loss: 0.8167
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7483 - val_loss: 0.8141
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7394 - val_loss: 0.8101
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7411 - val_loss: 0.8156
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7305 - val_loss: 0.8637
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7299 - val_loss: 0.8075
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7300 - val_loss: 0.8162
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7219 - val_loss: 0.8208
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7257 - val_loss: 0.8303
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7223 - val_loss: 0.8083
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7247 - val_loss: 0.7849
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7277 - val_loss: 0.7945
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7277 - val_loss: 0.8149
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7081 - val_loss: 0.8039
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7262 - val_loss: 0.8392
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7260 - val_loss: 0.8109
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7173 - val_loss: 0.8411
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7110 - val_loss: 0.7864
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7097 - val_loss: 0.8103
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7318 - val_loss: 0.7839
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7073 - val_loss: 0.8131
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7101 - val_loss: 0.8097
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7040 - val_loss: 0.7936
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6995 - val_loss: 0.8148
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6979 - val_loss: 0.7945
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7051 - val_loss: 0.8183
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7108 - val_loss: 0.8199
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7114 - val_loss: 0.8095
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7109 - val_loss: 0.8029
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6943 - val_loss: 0.8139
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6952 - val_loss: 0.8354
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7025 - val_loss: 0.7890
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6984 - val_loss: 0.7883
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6918 - val_loss: 0.7941
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6896 - val_loss: 0.8069
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6949 - val_loss: 0.8277
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6923 - val_loss: 0.8316
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6924 - val_loss: 0.7949
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6923 - val_loss: 0.8101
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6794 - val_loss: 0.7791
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6871 - val_loss: 0.7851
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6849 - val_loss: 0.7962
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6830 - val_loss: 0.7883
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6874 - val_loss: 0.7862
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6897 - val_loss: 0.7929
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6819 - val_loss: 0.7748
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6915 - val_loss: 0.8052
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6789 - val_loss: 0.8217
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6868 - val_loss: 0.7840
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6905 - val_loss: 0.7721
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6718 - val_loss: 0.7671
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6766 - val_loss: 0.7794
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6773 - val_loss: 0.8113
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6734 - val_loss: 0.7881
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6813 - val_loss: 0.7869
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6713 - val_loss: 0.7821
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6703 - val_loss: 0.7943
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6756 - val_loss: 0.7925
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6781 - val_loss: 0.7860
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6752 - val_loss: 0.7827
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6744 - val_loss: 0.8012
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6728 - val_loss: 0.8057
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6751 - val_loss: 0.7971
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6766 - val_loss: 0.7591
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6741 - val_loss: 0.7973
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6733 - val_loss: 0.8032
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6697 - val_loss: 0.7857
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6660 - val_loss: 0.7838
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_47 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_150 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_150 ( [()]                 0           tf_op_layer_Shape_150[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_15 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_150[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_15[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_45 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_15 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_45[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_151 (TensorFl [(2,)]               0           tf_op_layer_Add_15[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_153 (TensorFl [(2,)]               0           tf_op_layer_Add_15[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_152 (TensorFl [(2,)]               0           tf_op_layer_Add_15[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_154 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_156 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_155 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_157 (TensorFl [(2,)]               0           tf_op_layer_Add_15[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_159 (TensorFl [(2,)]               0           tf_op_layer_Add_15[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_158 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_151 ( [()]                 0           tf_op_layer_Shape_151[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_153 ( [()]                 0           tf_op_layer_Shape_153[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_152 ( [()]                 0           tf_op_layer_Shape_152[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_154 ( [()]                 0           tf_op_layer_Shape_154[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_156 ( [()]                 0           tf_op_layer_Shape_156[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_155 ( [()]                 0           tf_op_layer_Shape_155[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_157 ( [()]                 0           tf_op_layer_Shape_157[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_159 ( [()]                 0           tf_op_layer_Shape_159[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_158 ( [()]                 0           tf_op_layer_Shape_158[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_90/shape (T [(3,)]               0           tf_op_layer_strided_slice_151[0][
                                                                 tf_op_layer_strided_slice_153[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_91/shape (T [(3,)]               0           tf_op_layer_strided_slice_152[0][
                                                                 tf_op_layer_strided_slice_153[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_92/shape (T [(3,)]               0           tf_op_layer_strided_slice_154[0][
                                                                 tf_op_layer_strided_slice_156[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_93/shape (T [(3,)]               0           tf_op_layer_strided_slice_155[0][
                                                                 tf_op_layer_strided_slice_156[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_94/shape (T [(3,)]               0           tf_op_layer_strided_slice_157[0][
                                                                 tf_op_layer_strided_slice_159[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_95/shape (T [(3,)]               0           tf_op_layer_strided_slice_158[0][
                                                                 tf_op_layer_strided_slice_159[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_90 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_15[0][0]         
                                                                 tf_op_layer_Reshape_90/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_90/multiples ( [(3,)]               0           tf_op_layer_strided_slice_152[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_91 (TensorF [(1, None, None)]    0           tf_op_layer_Add_15[0][0]         
                                                                 tf_op_layer_Reshape_91/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_91/multiples ( [(3,)]               0           tf_op_layer_strided_slice_151[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_92 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_92/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_92/multiples ( [(3,)]               0           tf_op_layer_strided_slice_155[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_93 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_93/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_93/multiples ( [(3,)]               0           tf_op_layer_strided_slice_154[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_94 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_15[0][0]         
                                                                 tf_op_layer_Reshape_94/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_94/multiples ( [(3,)]               0           tf_op_layer_strided_slice_158[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_95 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_95/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_95/multiples ( [(3,)]               0           tf_op_layer_strided_slice_157[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_90 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_90[0][0]     
                                                                 tf_op_layer_Tile_90/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_91 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_91[0][0]     
                                                                 tf_op_layer_Tile_91/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_92 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_92[0][0]     
                                                                 tf_op_layer_Tile_92/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_93 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_93[0][0]     
                                                                 tf_op_layer_Tile_93/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_94 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_94[0][0]     
                                                                 tf_op_layer_Tile_94/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_95 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_95[0][0]     
                                                                 tf_op_layer_Tile_95/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Sub_75 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_90[0][0]        
                                                                 tf_op_layer_Tile_91[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_76 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_92[0][0]        
                                                                 tf_op_layer_Tile_93[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_77 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_94[0][0]        
                                                                 tf_op_layer_Tile_95[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_60 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_75[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_61 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_76[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_62 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_77[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_105 (TensorFlo [(None, None)]       0           tf_op_layer_Square_60[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_106 (TensorFlo [(None, None)]       0           tf_op_layer_Square_61[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_107 (TensorFlo [(None, None)]       0           tf_op_layer_Square_62[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_45 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_105[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_45 (TensorFlow [()]                 0           tf_op_layer_strided_slice_153[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_46 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_106[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_46 (TensorFlow [()]                 0           tf_op_layer_strided_slice_156[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_47 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_107[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_47 (TensorFlow [()]                 0           tf_op_layer_strided_slice_159[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_45 (TensorF [(None, None)]       0           tf_op_layer_Neg_45[0][0]         
                                                                 tf_op_layer_Cast_45[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_46 (TensorF [(None, None)]       0           tf_op_layer_Neg_46[0][0]         
                                                                 tf_op_layer_Cast_46[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_47 (TensorF [(None, None)]       0           tf_op_layer_Neg_47[0][0]         
                                                                 tf_op_layer_Cast_47[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_45 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_45[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_46 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_46[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_47 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_47[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_108 (TensorFlo [()]                 0           tf_op_layer_Exp_45[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_109 (TensorFlo [()]                 0           tf_op_layer_Exp_46[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_110 (TensorFlo [()]                 0           tf_op_layer_Exp_47[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_79 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_30 (TensorFlo [()]                 0           tf_op_layer_Mean_108[0][0]       
                                                                 tf_op_layer_Mean_109[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_46 (TensorFlowO [()]                 0           tf_op_layer_Mean_110[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_63 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_79[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_78 (TensorFlowO [()]                 0           tf_op_layer_AddV2_30[0][0]       
                                                                 tf_op_layer_Mul_46[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_15 (TensorFlowO [(None,)]            0           tf_op_layer_Square_63[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_47 (TensorFlowO [()]                 0           tf_op_layer_Sub_78[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_31 (TensorFlo [(None,)]            0           tf_op_layer_Sum_15[0][0]         
                                                                 tf_op_layer_Mul_47[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_111 (TensorFlo [()]                 0           tf_op_layer_AddV2_31[0][0]       
__________________________________________________________________________________________________
add_loss_15 (AddLoss)           ()                   0           tf_op_layer_Mean_111[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.8541 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.1216s). Check your callbacks.
24/24 [==============================] - 0s 17ms/step - loss: 8.1721 - val_loss: 5.0543
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 3.9117 - val_loss: 2.8149
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 2.3658 - val_loss: 2.1529
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.9998 - val_loss: 1.9307
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.7938 - val_loss: 1.7723
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6354 - val_loss: 1.6401
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5446 - val_loss: 1.5171
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4582 - val_loss: 1.3935
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3664 - val_loss: 1.3718
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2957 - val_loss: 1.3068
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2518 - val_loss: 1.2883
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2407 - val_loss: 1.2167
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2014 - val_loss: 1.2255
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2025 - val_loss: 1.1980
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1691 - val_loss: 1.1897
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1627 - val_loss: 1.1929
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1589 - val_loss: 1.1593
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1288 - val_loss: 1.1709
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1208 - val_loss: 1.1718
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1070 - val_loss: 1.1539
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1273 - val_loss: 1.1609
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1022 - val_loss: 1.1868
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1066 - val_loss: 1.1496
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0881 - val_loss: 1.1531
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0945 - val_loss: 1.1583
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0949 - val_loss: 1.1659
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0903 - val_loss: 1.1342
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0792 - val_loss: 1.1241
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0747 - val_loss: 1.1244
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0592 - val_loss: 1.1339
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0742 - val_loss: 1.1356
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0681 - val_loss: 1.1194
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0654 - val_loss: 1.1369
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0460 - val_loss: 1.1105
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0674 - val_loss: 1.1024
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0443 - val_loss: 1.0961
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0390 - val_loss: 1.0979
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0420 - val_loss: 1.1168
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0411 - val_loss: 1.1036
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0356 - val_loss: 1.0949
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0297 - val_loss: 1.1017
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0356 - val_loss: 1.1097
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0072 - val_loss: 1.0868
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0365 - val_loss: 1.1009
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0204 - val_loss: 1.0856
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0296 - val_loss: 1.0843
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0326 - val_loss: 1.0937
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0186 - val_loss: 1.0836
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0338 - val_loss: 1.0699
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0204 - val_loss: 1.0592
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0074 - val_loss: 1.0831
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0042 - val_loss: 1.0730
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0065 - val_loss: 1.0829
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0175 - val_loss: 1.0697
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0155 - val_loss: 1.0723
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0133 - val_loss: 1.0740
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0128 - val_loss: 1.0878
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9962 - val_loss: 1.0848
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0036 - val_loss: 1.0576
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0081 - val_loss: 1.0584
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0103 - val_loss: 1.0598
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0015 - val_loss: 1.0722
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0019 - val_loss: 1.0584
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9998 - val_loss: 1.0959
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9832 - val_loss: 1.0701
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9878 - val_loss: 1.0558
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9920 - val_loss: 1.0640
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9922 - val_loss: 1.0517
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9902 - val_loss: 1.0918
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9727 - val_loss: 1.0933
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9847 - val_loss: 1.0703
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9803 - val_loss: 1.0745
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9926 - val_loss: 1.0627
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9941 - val_loss: 1.0785
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9947 - val_loss: 1.0729
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9720 - val_loss: 1.0524
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9856 - val_loss: 1.0683
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9867 - val_loss: 1.0526
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9874 - val_loss: 1.0477
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9800 - val_loss: 1.0603
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9856 - val_loss: 1.0698
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9915 - val_loss: 1.0876
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9753 - val_loss: 1.0562
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9896 - val_loss: 1.0513
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9752 - val_loss: 1.0599
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9891 - val_loss: 1.0531
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9819 - val_loss: 1.0781
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9787 - val_loss: 1.0801
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9761 - val_loss: 1.0436
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9717 - val_loss: 1.0396
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9872 - val_loss: 1.0537
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9830 - val_loss: 1.0720
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9738 - val_loss: 1.0328
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9773 - val_loss: 1.0374
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9702 - val_loss: 1.0332
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9811 - val_loss: 1.0424
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9880 - val_loss: 1.0416
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9732 - val_loss: 1.0304
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9767 - val_loss: 1.0510
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9693 - val_loss: 1.0408
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.29 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.31 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 2.28 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.26 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 10.22 seconds.
Calculated PHATE in 13.09 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_48 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_49 (Dense)                (None, 32)           2080        dense_48[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_49[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_49[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_50 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_51 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_52 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_48 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_49 (Dense)                (None, 32)           2080        dense_48[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_49[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_49[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_160 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_160 ( [()]                 0           tf_op_layer_Shape_160[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_16 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_160[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_16[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_48 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_16 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_48[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_161 (TensorFl [(2,)]               0           tf_op_layer_Add_16[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_163 (TensorFl [(2,)]               0           tf_op_layer_Add_16[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_162 (TensorFl [(2,)]               0           tf_op_layer_Add_16[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_164 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_166 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_165 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_167 (TensorFl [(2,)]               0           tf_op_layer_Add_16[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_169 (TensorFl [(2,)]               0           tf_op_layer_Add_16[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_168 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_161 ( [()]                 0           tf_op_layer_Shape_161[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_163 ( [()]                 0           tf_op_layer_Shape_163[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_162 ( [()]                 0           tf_op_layer_Shape_162[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_164 ( [()]                 0           tf_op_layer_Shape_164[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_166 ( [()]                 0           tf_op_layer_Shape_166[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_165 ( [()]                 0           tf_op_layer_Shape_165[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_167 ( [()]                 0           tf_op_layer_Shape_167[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_169 ( [()]                 0           tf_op_layer_Shape_169[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_168 ( [()]                 0           tf_op_layer_Shape_168[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_96/shape (T [(3,)]               0           tf_op_layer_strided_slice_161[0][
                                                                 tf_op_layer_strided_slice_163[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_97/shape (T [(3,)]               0           tf_op_layer_strided_slice_162[0][
                                                                 tf_op_layer_strided_slice_163[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_98/shape (T [(3,)]               0           tf_op_layer_strided_slice_164[0][
                                                                 tf_op_layer_strided_slice_166[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_99/shape (T [(3,)]               0           tf_op_layer_strided_slice_165[0][
                                                                 tf_op_layer_strided_slice_166[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_100/shape ( [(3,)]               0           tf_op_layer_strided_slice_167[0][
                                                                 tf_op_layer_strided_slice_169[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_101/shape ( [(3,)]               0           tf_op_layer_strided_slice_168[0][
                                                                 tf_op_layer_strided_slice_169[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_96 (TensorF [(None, 1, None)]    0           tf_op_layer_Add_16[0][0]         
                                                                 tf_op_layer_Reshape_96/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_96/multiples ( [(3,)]               0           tf_op_layer_strided_slice_162[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_97 (TensorF [(1, None, None)]    0           tf_op_layer_Add_16[0][0]         
                                                                 tf_op_layer_Reshape_97/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_97/multiples ( [(3,)]               0           tf_op_layer_strided_slice_161[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_98 (TensorF [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_98/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_98/multiples ( [(3,)]               0           tf_op_layer_strided_slice_165[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_99 (TensorF [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_99/shape[0][0
__________________________________________________________________________________________________
tf_op_layer_Tile_99/multiples ( [(3,)]               0           tf_op_layer_strided_slice_164[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_100 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_16[0][0]         
                                                                 tf_op_layer_Reshape_100/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_100/multiples  [(3,)]               0           tf_op_layer_strided_slice_168[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_101 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_101/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_101/multiples  [(3,)]               0           tf_op_layer_strided_slice_167[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_96 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_96[0][0]     
                                                                 tf_op_layer_Tile_96/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_97 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_97[0][0]     
                                                                 tf_op_layer_Tile_97/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_98 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_98[0][0]     
                                                                 tf_op_layer_Tile_98/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_99 (TensorFlow [(None, None, None)] 0           tf_op_layer_Reshape_99[0][0]     
                                                                 tf_op_layer_Tile_99/multiples[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_100 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_100[0][0]    
                                                                 tf_op_layer_Tile_100/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_101 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_101[0][0]    
                                                                 tf_op_layer_Tile_101/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_80 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_96[0][0]        
                                                                 tf_op_layer_Tile_97[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_81 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_98[0][0]        
                                                                 tf_op_layer_Tile_99[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_82 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_100[0][0]       
                                                                 tf_op_layer_Tile_101[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_64 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_80[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_65 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_81[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_66 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_82[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_112 (TensorFlo [(None, None)]       0           tf_op_layer_Square_64[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_113 (TensorFlo [(None, None)]       0           tf_op_layer_Square_65[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_114 (TensorFlo [(None, None)]       0           tf_op_layer_Square_66[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_48 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_112[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_48 (TensorFlow [()]                 0           tf_op_layer_strided_slice_163[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_49 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_113[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_49 (TensorFlow [()]                 0           tf_op_layer_strided_slice_166[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_50 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_114[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_50 (TensorFlow [()]                 0           tf_op_layer_strided_slice_169[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_48 (TensorF [(None, None)]       0           tf_op_layer_Neg_48[0][0]         
                                                                 tf_op_layer_Cast_48[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_49 (TensorF [(None, None)]       0           tf_op_layer_Neg_49[0][0]         
                                                                 tf_op_layer_Cast_49[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_50 (TensorF [(None, None)]       0           tf_op_layer_Neg_50[0][0]         
                                                                 tf_op_layer_Cast_50[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_48 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_48[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_49 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_49[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_50 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_50[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_115 (TensorFlo [()]                 0           tf_op_layer_Exp_48[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_116 (TensorFlo [()]                 0           tf_op_layer_Exp_49[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_117 (TensorFlo [()]                 0           tf_op_layer_Exp_50[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_84 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_32 (TensorFlo [()]                 0           tf_op_layer_Mean_115[0][0]       
                                                                 tf_op_layer_Mean_116[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_49 (TensorFlowO [()]                 0           tf_op_layer_Mean_117[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_67 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_84[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_83 (TensorFlowO [()]                 0           tf_op_layer_AddV2_32[0][0]       
                                                                 tf_op_layer_Mul_49[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_16 (TensorFlowO [(None,)]            0           tf_op_layer_Square_67[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_50 (TensorFlowO [()]                 0           tf_op_layer_Sub_83[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_33 (TensorFlo [(None,)]            0           tf_op_layer_Sum_16[0][0]         
                                                                 tf_op_layer_Mul_50[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_118 (TensorFlo [()]                 0           tf_op_layer_AddV2_33[0][0]       
__________________________________________________________________________________________________
add_loss_16 (AddLoss)           ()                   0           tf_op_layer_Mean_118[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.1180WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.2256s). Check your callbacks.
24/24 [==============================] - 1s 31ms/step - loss: 4.7247 - val_loss: 2.0521
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6894 - val_loss: 1.3266
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2907 - val_loss: 1.3331
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2278 - val_loss: 1.1805
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1425 - val_loss: 1.0807
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0349 - val_loss: 0.9394
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9545 - val_loss: 0.8796
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8854 - val_loss: 0.8813
Epoch 9/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8493 - val_loss: 0.8295
Epoch 10/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8481 - val_loss: 0.8315
Epoch 11/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8318 - val_loss: 0.8302
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8175 - val_loss: 0.8598
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8367 - val_loss: 0.8472
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8133 - val_loss: 0.8441
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8008 - val_loss: 0.8151
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8013 - val_loss: 0.8363
Epoch 17/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7926 - val_loss: 0.8044
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7888 - val_loss: 0.8113
Epoch 19/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7846 - val_loss: 0.8467
Epoch 20/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7882 - val_loss: 0.7926
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7870 - val_loss: 0.8099
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7789 - val_loss: 0.7906
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7811 - val_loss: 0.8156
Epoch 24/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7778 - val_loss: 0.7886
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7701 - val_loss: 0.7992
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7658 - val_loss: 0.7911
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7672 - val_loss: 0.7779
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7552 - val_loss: 0.8037
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7540 - val_loss: 0.7831
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7561 - val_loss: 0.8139
Epoch 31/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7574 - val_loss: 0.8016
Epoch 32/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7578 - val_loss: 0.8173
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7474 - val_loss: 0.8001
Epoch 34/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7421 - val_loss: 0.7702
Epoch 35/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7524 - val_loss: 0.7920
Epoch 36/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7525 - val_loss: 0.7786
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7431 - val_loss: 0.7698
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7460 - val_loss: 0.7645
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7528 - val_loss: 0.7745
Epoch 40/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7499 - val_loss: 0.7633
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7371 - val_loss: 0.7640
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7441 - val_loss: 0.8087
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7364 - val_loss: 0.7739
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7316 - val_loss: 0.7643
Epoch 45/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7269 - val_loss: 0.7723
Epoch 46/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7322 - val_loss: 0.7694
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7236 - val_loss: 0.7851
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7270 - val_loss: 0.7656
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7244 - val_loss: 0.7994
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7306 - val_loss: 0.7542
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7283 - val_loss: 0.7580
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7260 - val_loss: 0.7722
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7293 - val_loss: 0.7738
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7161 - val_loss: 0.7825
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7184 - val_loss: 0.7791
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7216 - val_loss: 0.7744
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7137 - val_loss: 0.7681
Epoch 58/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7228 - val_loss: 0.7837
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7196 - val_loss: 0.7708
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7297 - val_loss: 0.7713
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7152 - val_loss: 0.7877
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7206 - val_loss: 0.7718
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7107 - val_loss: 0.7651
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7088 - val_loss: 0.7654
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7108 - val_loss: 0.7744
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7173 - val_loss: 0.7758
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7267 - val_loss: 0.7749
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7165 - val_loss: 0.7746
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7342 - val_loss: 0.7764
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7114 - val_loss: 0.7618
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7072 - val_loss: 0.7893
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7099 - val_loss: 0.7502
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7199 - val_loss: 0.7619
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7116 - val_loss: 0.7662
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7055 - val_loss: 0.7727
Epoch 76/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7200 - val_loss: 0.7954
Epoch 77/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7146 - val_loss: 0.7610
Epoch 78/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7121 - val_loss: 0.7559
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7035 - val_loss: 0.7967
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7168 - val_loss: 0.7554
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7118 - val_loss: 0.7738
Epoch 82/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7076 - val_loss: 0.7530
Epoch 83/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7236 - val_loss: 0.7814
Epoch 84/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7042 - val_loss: 0.7693
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7038 - val_loss: 0.7590
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7066 - val_loss: 0.7481
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7127 - val_loss: 0.7598
Epoch 88/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7169 - val_loss: 0.7628
Epoch 89/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7006 - val_loss: 0.7714
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7036 - val_loss: 0.7572
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7044 - val_loss: 0.7668
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7120 - val_loss: 0.8085
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7003 - val_loss: 0.7633
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7029 - val_loss: 0.7756
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7043 - val_loss: 0.7557
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7153 - val_loss: 0.7714
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7068 - val_loss: 0.7819
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6981 - val_loss: 0.7592
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7050 - val_loss: 0.7618
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7024 - val_loss: 0.7715
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_53 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_170 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_170 ( [()]                 0           tf_op_layer_Shape_170[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_17 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_170[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_17[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_51 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_17 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_51[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_171 (TensorFl [(2,)]               0           tf_op_layer_Add_17[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_173 (TensorFl [(2,)]               0           tf_op_layer_Add_17[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_172 (TensorFl [(2,)]               0           tf_op_layer_Add_17[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_174 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_176 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_175 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_177 (TensorFl [(2,)]               0           tf_op_layer_Add_17[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_179 (TensorFl [(2,)]               0           tf_op_layer_Add_17[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_178 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_171 ( [()]                 0           tf_op_layer_Shape_171[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_173 ( [()]                 0           tf_op_layer_Shape_173[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_172 ( [()]                 0           tf_op_layer_Shape_172[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_174 ( [()]                 0           tf_op_layer_Shape_174[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_176 ( [()]                 0           tf_op_layer_Shape_176[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_175 ( [()]                 0           tf_op_layer_Shape_175[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_177 ( [()]                 0           tf_op_layer_Shape_177[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_179 ( [()]                 0           tf_op_layer_Shape_179[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_178 ( [()]                 0           tf_op_layer_Shape_178[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_102/shape ( [(3,)]               0           tf_op_layer_strided_slice_171[0][
                                                                 tf_op_layer_strided_slice_173[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_103/shape ( [(3,)]               0           tf_op_layer_strided_slice_172[0][
                                                                 tf_op_layer_strided_slice_173[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_104/shape ( [(3,)]               0           tf_op_layer_strided_slice_174[0][
                                                                 tf_op_layer_strided_slice_176[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_105/shape ( [(3,)]               0           tf_op_layer_strided_slice_175[0][
                                                                 tf_op_layer_strided_slice_176[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_106/shape ( [(3,)]               0           tf_op_layer_strided_slice_177[0][
                                                                 tf_op_layer_strided_slice_179[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_107/shape ( [(3,)]               0           tf_op_layer_strided_slice_178[0][
                                                                 tf_op_layer_strided_slice_179[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_102 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_17[0][0]         
                                                                 tf_op_layer_Reshape_102/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_102/multiples  [(3,)]               0           tf_op_layer_strided_slice_172[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_103 (Tensor [(1, None, None)]    0           tf_op_layer_Add_17[0][0]         
                                                                 tf_op_layer_Reshape_103/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_103/multiples  [(3,)]               0           tf_op_layer_strided_slice_171[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_104 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_104/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_104/multiples  [(3,)]               0           tf_op_layer_strided_slice_175[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_105 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_105/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_105/multiples  [(3,)]               0           tf_op_layer_strided_slice_174[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_106 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_17[0][0]         
                                                                 tf_op_layer_Reshape_106/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_106/multiples  [(3,)]               0           tf_op_layer_strided_slice_178[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_107 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_107/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_107/multiples  [(3,)]               0           tf_op_layer_strided_slice_177[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_102 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_102[0][0]    
                                                                 tf_op_layer_Tile_102/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_103 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_103[0][0]    
                                                                 tf_op_layer_Tile_103/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_104 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_104[0][0]    
                                                                 tf_op_layer_Tile_104/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_105 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_105[0][0]    
                                                                 tf_op_layer_Tile_105/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_106 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_106[0][0]    
                                                                 tf_op_layer_Tile_106/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_107 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_107[0][0]    
                                                                 tf_op_layer_Tile_107/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_85 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_102[0][0]       
                                                                 tf_op_layer_Tile_103[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_86 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_104[0][0]       
                                                                 tf_op_layer_Tile_105[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_87 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_106[0][0]       
                                                                 tf_op_layer_Tile_107[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_68 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_85[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_69 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_86[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_70 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_87[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_119 (TensorFlo [(None, None)]       0           tf_op_layer_Square_68[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_120 (TensorFlo [(None, None)]       0           tf_op_layer_Square_69[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_121 (TensorFlo [(None, None)]       0           tf_op_layer_Square_70[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_51 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_119[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_51 (TensorFlow [()]                 0           tf_op_layer_strided_slice_173[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_52 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_120[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_52 (TensorFlow [()]                 0           tf_op_layer_strided_slice_176[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_53 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_121[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_53 (TensorFlow [()]                 0           tf_op_layer_strided_slice_179[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_51 (TensorF [(None, None)]       0           tf_op_layer_Neg_51[0][0]         
                                                                 tf_op_layer_Cast_51[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_52 (TensorF [(None, None)]       0           tf_op_layer_Neg_52[0][0]         
                                                                 tf_op_layer_Cast_52[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_53 (TensorF [(None, None)]       0           tf_op_layer_Neg_53[0][0]         
                                                                 tf_op_layer_Cast_53[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_51 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_51[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_52 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_52[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_53 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_53[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_122 (TensorFlo [()]                 0           tf_op_layer_Exp_51[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_123 (TensorFlo [()]                 0           tf_op_layer_Exp_52[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_124 (TensorFlo [()]                 0           tf_op_layer_Exp_53[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_89 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_34 (TensorFlo [()]                 0           tf_op_layer_Mean_122[0][0]       
                                                                 tf_op_layer_Mean_123[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_52 (TensorFlowO [()]                 0           tf_op_layer_Mean_124[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_71 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_89[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_88 (TensorFlowO [()]                 0           tf_op_layer_AddV2_34[0][0]       
                                                                 tf_op_layer_Mul_52[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_17 (TensorFlowO [(None,)]            0           tf_op_layer_Square_71[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_53 (TensorFlowO [()]                 0           tf_op_layer_Sub_88[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_35 (TensorFlo [(None,)]            0           tf_op_layer_Sum_17[0][0]         
                                                                 tf_op_layer_Mul_53[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_125 (TensorFlo [()]                 0           tf_op_layer_AddV2_35[0][0]       
__________________________________________________________________________________________________
add_loss_17 (AddLoss)           ()                   0           tf_op_layer_Mean_125[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.6191WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.1894s). Check your callbacks.
24/24 [==============================] - 1s 24ms/step - loss: 7.9790 - val_loss: 5.4275
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 3.8534 - val_loss: 2.8778
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.4060 - val_loss: 2.0975
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.9719 - val_loss: 1.9366
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8200 - val_loss: 1.7888
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7110 - val_loss: 1.7017
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6191 - val_loss: 1.6583
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5479 - val_loss: 1.5808
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4781 - val_loss: 1.4807
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4366 - val_loss: 1.4480
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3647 - val_loss: 1.4011
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3190 - val_loss: 1.3325
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2900 - val_loss: 1.3003
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2524 - val_loss: 1.2881
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2113 - val_loss: 1.2205
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1857 - val_loss: 1.2073
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1672 - val_loss: 1.1664
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1533 - val_loss: 1.1589
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1468 - val_loss: 1.1892
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1403 - val_loss: 1.1813
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1218 - val_loss: 1.1723
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1198 - val_loss: 1.1320
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1134 - val_loss: 1.1632
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1002 - val_loss: 1.1402
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1041 - val_loss: 1.1107
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0854 - val_loss: 1.1102
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0925 - val_loss: 1.1267
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0814 - val_loss: 1.1326
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0766 - val_loss: 1.1401
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0639 - val_loss: 1.0857
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0576 - val_loss: 1.1019
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0650 - val_loss: 1.1056
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0545 - val_loss: 1.0887
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0526 - val_loss: 1.0941
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0720 - val_loss: 1.1095
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0688 - val_loss: 1.1024
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0524 - val_loss: 1.0801
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0587 - val_loss: 1.1005
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0550 - val_loss: 1.0516
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0357 - val_loss: 1.1264
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0455 - val_loss: 1.1176
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0424 - val_loss: 1.0538
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0254 - val_loss: 1.0690
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0465 - val_loss: 1.0325
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0273 - val_loss: 1.0509
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0355 - val_loss: 1.0751
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0217 - val_loss: 1.0597
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0280 - val_loss: 1.0353
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0249 - val_loss: 1.0499
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0239 - val_loss: 1.0781
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0165 - val_loss: 1.1080
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0217 - val_loss: 1.0441
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0274 - val_loss: 1.0272
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0289 - val_loss: 1.0629
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0151 - val_loss: 1.0324
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0234 - val_loss: 1.0712
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0189 - val_loss: 1.0824
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0114 - val_loss: 1.0302
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0060 - val_loss: 1.0641
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0007 - val_loss: 1.0231
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0105 - val_loss: 1.0872
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0112 - val_loss: 1.0367
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0030 - val_loss: 1.0723
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0062 - val_loss: 1.0401
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0086 - val_loss: 1.0335
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0133 - val_loss: 1.0299
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0038 - val_loss: 1.0392
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9914 - val_loss: 1.0379
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0102 - val_loss: 1.0075
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0101 - val_loss: 1.0653
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0026 - val_loss: 1.0019
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9923 - val_loss: 1.0195
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9952 - val_loss: 1.0173
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9938 - val_loss: 1.0351
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9999 - val_loss: 1.0453
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9957 - val_loss: 1.0165
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0064 - val_loss: 1.0489
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9851 - val_loss: 1.0020
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9934 - val_loss: 1.0373
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0055 - val_loss: 1.0014
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0004 - val_loss: 0.9941
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9900 - val_loss: 1.0106
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9981 - val_loss: 1.0392
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9964 - val_loss: 1.0318
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9821 - val_loss: 1.0195
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9848 - val_loss: 1.0094
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9970 - val_loss: 0.9996
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9926 - val_loss: 1.0081
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9840 - val_loss: 0.9999
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9877 - val_loss: 1.0233
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9854 - val_loss: 1.0106
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9942 - val_loss: 1.0104
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9896 - val_loss: 1.0619
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9790 - val_loss: 1.0171
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9786 - val_loss: 1.0224
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9887 - val_loss: 1.0232
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9778 - val_loss: 1.0336
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9779 - val_loss: 0.9936
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9769 - val_loss: 1.0099
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9755 - val_loss: 1.0421
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.28 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.30 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.31 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.21 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 6.26 seconds.
Calculated PHATE in 8.09 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_54 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_55 (Dense)                (None, 32)           2080        dense_54[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_55[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_55[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_56 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_57 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_58 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_54 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_55 (Dense)                (None, 32)           2080        dense_54[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_55[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_55[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_180 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_180 ( [()]                 0           tf_op_layer_Shape_180[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_18 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_180[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_18[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_54 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_18 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_54[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_181 (TensorFl [(2,)]               0           tf_op_layer_Add_18[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_183 (TensorFl [(2,)]               0           tf_op_layer_Add_18[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_182 (TensorFl [(2,)]               0           tf_op_layer_Add_18[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_184 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_186 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_185 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_187 (TensorFl [(2,)]               0           tf_op_layer_Add_18[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_189 (TensorFl [(2,)]               0           tf_op_layer_Add_18[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_188 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_181 ( [()]                 0           tf_op_layer_Shape_181[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_183 ( [()]                 0           tf_op_layer_Shape_183[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_182 ( [()]                 0           tf_op_layer_Shape_182[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_184 ( [()]                 0           tf_op_layer_Shape_184[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_186 ( [()]                 0           tf_op_layer_Shape_186[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_185 ( [()]                 0           tf_op_layer_Shape_185[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_187 ( [()]                 0           tf_op_layer_Shape_187[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_189 ( [()]                 0           tf_op_layer_Shape_189[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_188 ( [()]                 0           tf_op_layer_Shape_188[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_108/shape ( [(3,)]               0           tf_op_layer_strided_slice_181[0][
                                                                 tf_op_layer_strided_slice_183[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_109/shape ( [(3,)]               0           tf_op_layer_strided_slice_182[0][
                                                                 tf_op_layer_strided_slice_183[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_110/shape ( [(3,)]               0           tf_op_layer_strided_slice_184[0][
                                                                 tf_op_layer_strided_slice_186[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_111/shape ( [(3,)]               0           tf_op_layer_strided_slice_185[0][
                                                                 tf_op_layer_strided_slice_186[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_112/shape ( [(3,)]               0           tf_op_layer_strided_slice_187[0][
                                                                 tf_op_layer_strided_slice_189[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_113/shape ( [(3,)]               0           tf_op_layer_strided_slice_188[0][
                                                                 tf_op_layer_strided_slice_189[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_108 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_18[0][0]         
                                                                 tf_op_layer_Reshape_108/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_108/multiples  [(3,)]               0           tf_op_layer_strided_slice_182[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_109 (Tensor [(1, None, None)]    0           tf_op_layer_Add_18[0][0]         
                                                                 tf_op_layer_Reshape_109/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_109/multiples  [(3,)]               0           tf_op_layer_strided_slice_181[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_110 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_110/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_110/multiples  [(3,)]               0           tf_op_layer_strided_slice_185[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_111 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_111/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_111/multiples  [(3,)]               0           tf_op_layer_strided_slice_184[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_112 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_18[0][0]         
                                                                 tf_op_layer_Reshape_112/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_112/multiples  [(3,)]               0           tf_op_layer_strided_slice_188[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_113 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_113/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_113/multiples  [(3,)]               0           tf_op_layer_strided_slice_187[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_108 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_108[0][0]    
                                                                 tf_op_layer_Tile_108/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_109 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_109[0][0]    
                                                                 tf_op_layer_Tile_109/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_110 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_110[0][0]    
                                                                 tf_op_layer_Tile_110/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_111 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_111[0][0]    
                                                                 tf_op_layer_Tile_111/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_112 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_112[0][0]    
                                                                 tf_op_layer_Tile_112/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_113 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_113[0][0]    
                                                                 tf_op_layer_Tile_113/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_90 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_108[0][0]       
                                                                 tf_op_layer_Tile_109[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_91 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_110[0][0]       
                                                                 tf_op_layer_Tile_111[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_92 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_112[0][0]       
                                                                 tf_op_layer_Tile_113[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_72 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_90[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_73 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_91[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_74 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_92[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_126 (TensorFlo [(None, None)]       0           tf_op_layer_Square_72[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_127 (TensorFlo [(None, None)]       0           tf_op_layer_Square_73[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_128 (TensorFlo [(None, None)]       0           tf_op_layer_Square_74[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_54 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_126[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_54 (TensorFlow [()]                 0           tf_op_layer_strided_slice_183[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_55 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_127[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_55 (TensorFlow [()]                 0           tf_op_layer_strided_slice_186[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_56 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_128[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_56 (TensorFlow [()]                 0           tf_op_layer_strided_slice_189[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_54 (TensorF [(None, None)]       0           tf_op_layer_Neg_54[0][0]         
                                                                 tf_op_layer_Cast_54[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_55 (TensorF [(None, None)]       0           tf_op_layer_Neg_55[0][0]         
                                                                 tf_op_layer_Cast_55[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_56 (TensorF [(None, None)]       0           tf_op_layer_Neg_56[0][0]         
                                                                 tf_op_layer_Cast_56[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_54 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_54[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_55 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_55[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_56 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_56[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_129 (TensorFlo [()]                 0           tf_op_layer_Exp_54[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_130 (TensorFlo [()]                 0           tf_op_layer_Exp_55[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_131 (TensorFlo [()]                 0           tf_op_layer_Exp_56[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_94 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_36 (TensorFlo [()]                 0           tf_op_layer_Mean_129[0][0]       
                                                                 tf_op_layer_Mean_130[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_55 (TensorFlowO [()]                 0           tf_op_layer_Mean_131[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_75 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_94[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_93 (TensorFlowO [()]                 0           tf_op_layer_AddV2_36[0][0]       
                                                                 tf_op_layer_Mul_55[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_18 (TensorFlowO [(None,)]            0           tf_op_layer_Square_75[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_56 (TensorFlowO [()]                 0           tf_op_layer_Sub_93[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_37 (TensorFlo [(None,)]            0           tf_op_layer_Sum_18[0][0]         
                                                                 tf_op_layer_Mul_56[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_132 (TensorFlo [()]                 0           tf_op_layer_AddV2_37[0][0]       
__________________________________________________________________________________________________
add_loss_18 (AddLoss)           ()                   0           tf_op_layer_Mean_132[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 9.8427 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.2242s). Check your callbacks.
24/24 [==============================] - 1s 26ms/step - loss: 5.0933 - val_loss: 2.3143
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8275 - val_loss: 1.3331
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3399 - val_loss: 1.2231
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2281 - val_loss: 1.1482
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1406 - val_loss: 1.0987
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1182 - val_loss: 1.0809
Epoch 7/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0523 - val_loss: 1.0314
Epoch 8/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9781 - val_loss: 0.9619
Epoch 9/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8960 - val_loss: 0.8886
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8614 - val_loss: 0.8587
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8429 - val_loss: 0.8371
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8307 - val_loss: 0.8582
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8366 - val_loss: 0.8445
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8239 - val_loss: 0.8357
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7996 - val_loss: 0.8572
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8066 - val_loss: 0.8244
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8033 - val_loss: 0.8246
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7945 - val_loss: 0.8183
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7780 - val_loss: 0.8258
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7884 - val_loss: 0.8249
Epoch 21/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7814 - val_loss: 0.8341
Epoch 22/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7848 - val_loss: 0.8283
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7685 - val_loss: 0.8201
Epoch 24/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7580 - val_loss: 0.7877
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7644 - val_loss: 0.8020
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7614 - val_loss: 0.7897
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7507 - val_loss: 0.7842
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7573 - val_loss: 0.7819
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7482 - val_loss: 0.7915
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7441 - val_loss: 0.7939
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7358 - val_loss: 0.7779
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7387 - val_loss: 0.7771
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7393 - val_loss: 0.7624
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7300 - val_loss: 0.7707
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7377 - val_loss: 0.7816
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7324 - val_loss: 0.7618
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7301 - val_loss: 0.7843
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7229 - val_loss: 0.7744
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7203 - val_loss: 0.7611
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7241 - val_loss: 0.8132
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7198 - val_loss: 0.7482
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7180 - val_loss: 0.7628
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7196 - val_loss: 0.7877
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7229 - val_loss: 0.7765
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7233 - val_loss: 0.7479
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7020 - val_loss: 0.7917
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7166 - val_loss: 0.7658
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6996 - val_loss: 0.7641
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7058 - val_loss: 0.7540
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7085 - val_loss: 0.7569
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6953 - val_loss: 0.7462
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7047 - val_loss: 0.7720
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6965 - val_loss: 0.7356
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7051 - val_loss: 0.7405
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6979 - val_loss: 0.7398
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7049 - val_loss: 0.7475
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7018 - val_loss: 0.7530
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6943 - val_loss: 0.7523
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6991 - val_loss: 0.7584
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6980 - val_loss: 0.7393
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6843 - val_loss: 0.7362
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6948 - val_loss: 0.7218
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6915 - val_loss: 0.7479
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6879 - val_loss: 0.7336
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6894 - val_loss: 0.7686
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6859 - val_loss: 0.7305
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6881 - val_loss: 0.7301
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6882 - val_loss: 0.7663
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6756 - val_loss: 0.7483
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6942 - val_loss: 0.7381
Epoch 71/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6788 - val_loss: 0.7408
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6854 - val_loss: 0.7323
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6806 - val_loss: 0.7229
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6842 - val_loss: 0.7349
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6811 - val_loss: 0.7247
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6736 - val_loss: 0.7308
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6752 - val_loss: 0.7421
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6793 - val_loss: 0.7348
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6787 - val_loss: 0.7310
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6720 - val_loss: 0.8134
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6915 - val_loss: 0.7274
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6752 - val_loss: 0.7286
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6686 - val_loss: 0.7498
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6800 - val_loss: 0.7160
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6733 - val_loss: 0.7479
Epoch 86/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6632 - val_loss: 0.7128
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6739 - val_loss: 0.7212
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6706 - val_loss: 0.7167
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6708 - val_loss: 0.7294
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6672 - val_loss: 0.7197
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6721 - val_loss: 0.7115
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6683 - val_loss: 0.7355
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6728 - val_loss: 0.7467
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6682 - val_loss: 0.7138
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6754 - val_loss: 0.7179
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6646 - val_loss: 0.7425
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6643 - val_loss: 0.7425
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6668 - val_loss: 0.7233
Epoch 99/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6696 - val_loss: 0.7054
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6604 - val_loss: 0.7142
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_59 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_190 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_190 ( [()]                 0           tf_op_layer_Shape_190[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_19 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_190[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_19[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_57 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_19 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_57[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_191 (TensorFl [(2,)]               0           tf_op_layer_Add_19[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_193 (TensorFl [(2,)]               0           tf_op_layer_Add_19[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_192 (TensorFl [(2,)]               0           tf_op_layer_Add_19[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_194 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_196 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_195 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_197 (TensorFl [(2,)]               0           tf_op_layer_Add_19[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_199 (TensorFl [(2,)]               0           tf_op_layer_Add_19[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_198 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_191 ( [()]                 0           tf_op_layer_Shape_191[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_193 ( [()]                 0           tf_op_layer_Shape_193[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_192 ( [()]                 0           tf_op_layer_Shape_192[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_194 ( [()]                 0           tf_op_layer_Shape_194[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_196 ( [()]                 0           tf_op_layer_Shape_196[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_195 ( [()]                 0           tf_op_layer_Shape_195[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_197 ( [()]                 0           tf_op_layer_Shape_197[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_199 ( [()]                 0           tf_op_layer_Shape_199[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_198 ( [()]                 0           tf_op_layer_Shape_198[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_114/shape ( [(3,)]               0           tf_op_layer_strided_slice_191[0][
                                                                 tf_op_layer_strided_slice_193[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_115/shape ( [(3,)]               0           tf_op_layer_strided_slice_192[0][
                                                                 tf_op_layer_strided_slice_193[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_116/shape ( [(3,)]               0           tf_op_layer_strided_slice_194[0][
                                                                 tf_op_layer_strided_slice_196[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_117/shape ( [(3,)]               0           tf_op_layer_strided_slice_195[0][
                                                                 tf_op_layer_strided_slice_196[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_118/shape ( [(3,)]               0           tf_op_layer_strided_slice_197[0][
                                                                 tf_op_layer_strided_slice_199[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_119/shape ( [(3,)]               0           tf_op_layer_strided_slice_198[0][
                                                                 tf_op_layer_strided_slice_199[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_114 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_19[0][0]         
                                                                 tf_op_layer_Reshape_114/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_114/multiples  [(3,)]               0           tf_op_layer_strided_slice_192[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_115 (Tensor [(1, None, None)]    0           tf_op_layer_Add_19[0][0]         
                                                                 tf_op_layer_Reshape_115/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_115/multiples  [(3,)]               0           tf_op_layer_strided_slice_191[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_116 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_116/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_116/multiples  [(3,)]               0           tf_op_layer_strided_slice_195[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_117 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_117/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_117/multiples  [(3,)]               0           tf_op_layer_strided_slice_194[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_118 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_19[0][0]         
                                                                 tf_op_layer_Reshape_118/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_118/multiples  [(3,)]               0           tf_op_layer_strided_slice_198[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_119 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_119/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_119/multiples  [(3,)]               0           tf_op_layer_strided_slice_197[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_114 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_114[0][0]    
                                                                 tf_op_layer_Tile_114/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_115 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_115[0][0]    
                                                                 tf_op_layer_Tile_115/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_116 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_116[0][0]    
                                                                 tf_op_layer_Tile_116/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_117 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_117[0][0]    
                                                                 tf_op_layer_Tile_117/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_118 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_118[0][0]    
                                                                 tf_op_layer_Tile_118/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_119 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_119[0][0]    
                                                                 tf_op_layer_Tile_119/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_95 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_114[0][0]       
                                                                 tf_op_layer_Tile_115[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_96 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_116[0][0]       
                                                                 tf_op_layer_Tile_117[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_97 (TensorFlowO [(None, None, None)] 0           tf_op_layer_Tile_118[0][0]       
                                                                 tf_op_layer_Tile_119[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_76 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_95[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_77 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_96[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Square_78 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_97[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_133 (TensorFlo [(None, None)]       0           tf_op_layer_Square_76[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_134 (TensorFlo [(None, None)]       0           tf_op_layer_Square_77[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_135 (TensorFlo [(None, None)]       0           tf_op_layer_Square_78[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_57 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_133[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_57 (TensorFlow [()]                 0           tf_op_layer_strided_slice_193[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_58 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_134[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_58 (TensorFlow [()]                 0           tf_op_layer_strided_slice_196[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_59 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_135[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_59 (TensorFlow [()]                 0           tf_op_layer_strided_slice_199[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_57 (TensorF [(None, None)]       0           tf_op_layer_Neg_57[0][0]         
                                                                 tf_op_layer_Cast_57[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_58 (TensorF [(None, None)]       0           tf_op_layer_Neg_58[0][0]         
                                                                 tf_op_layer_Cast_58[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_59 (TensorF [(None, None)]       0           tf_op_layer_Neg_59[0][0]         
                                                                 tf_op_layer_Cast_59[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_57 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_57[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_58 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_58[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_59 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_59[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_136 (TensorFlo [()]                 0           tf_op_layer_Exp_57[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_137 (TensorFlo [()]                 0           tf_op_layer_Exp_58[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_138 (TensorFlo [()]                 0           tf_op_layer_Exp_59[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_99 (TensorFlowO [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_38 (TensorFlo [()]                 0           tf_op_layer_Mean_136[0][0]       
                                                                 tf_op_layer_Mean_137[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_58 (TensorFlowO [()]                 0           tf_op_layer_Mean_138[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_79 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_99[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_98 (TensorFlowO [()]                 0           tf_op_layer_AddV2_38[0][0]       
                                                                 tf_op_layer_Mul_58[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_19 (TensorFlowO [(None,)]            0           tf_op_layer_Square_79[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_59 (TensorFlowO [()]                 0           tf_op_layer_Sub_98[0][0]         
__________________________________________________________________________________________________
tf_op_layer_AddV2_39 (TensorFlo [(None,)]            0           tf_op_layer_Sum_19[0][0]         
                                                                 tf_op_layer_Mul_59[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_139 (TensorFlo [()]                 0           tf_op_layer_AddV2_39[0][0]       
__________________________________________________________________________________________________
add_loss_19 (AddLoss)           ()                   0           tf_op_layer_Mean_139[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.2813WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.1998s). Check your callbacks.
24/24 [==============================] - 1s 25ms/step - loss: 8.1076 - val_loss: 5.0133
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 3.9133 - val_loss: 2.6937
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 2.3176 - val_loss: 2.1397
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.9711 - val_loss: 1.9713
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8363 - val_loss: 1.8424
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.7432 - val_loss: 1.7756
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6784 - val_loss: 1.7001
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6158 - val_loss: 1.6739
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5511 - val_loss: 1.5831
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5172 - val_loss: 1.5719
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4768 - val_loss: 1.5363
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4410 - val_loss: 1.5240
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4138 - val_loss: 1.4327
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3776 - val_loss: 1.4056
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3319 - val_loss: 1.3657
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2976 - val_loss: 1.3313
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2652 - val_loss: 1.2797
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2644 - val_loss: 1.2510
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2320 - val_loss: 1.2319
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1960 - val_loss: 1.1944
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1786 - val_loss: 1.2023
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1761 - val_loss: 1.1757
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1612 - val_loss: 1.1551
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1534 - val_loss: 1.1607
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1399 - val_loss: 1.1777
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1139 - val_loss: 1.1574
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1278 - val_loss: 1.1499
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1076 - val_loss: 1.1399
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1205 - val_loss: 1.1287
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0946 - val_loss: 1.1448
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1056 - val_loss: 1.1522
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0947 - val_loss: 1.0977
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0892 - val_loss: 1.1230
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0841 - val_loss: 1.0995
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0748 - val_loss: 1.1318
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0663 - val_loss: 1.1151
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0759 - val_loss: 1.0914
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0724 - val_loss: 1.0963
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0676 - val_loss: 1.1085
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0674 - val_loss: 1.0809
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0627 - val_loss: 1.0968
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0632 - val_loss: 1.0720
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0642 - val_loss: 1.0872
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0361 - val_loss: 1.0764
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0713 - val_loss: 1.0813
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0525 - val_loss: 1.0964
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0364 - val_loss: 1.0989
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0306 - val_loss: 1.0744
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0412 - val_loss: 1.0774
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0219 - val_loss: 1.0850
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0358 - val_loss: 1.0880
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0205 - val_loss: 1.0759
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0250 - val_loss: 1.0964
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0236 - val_loss: 1.0711
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0328 - val_loss: 1.0934
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0234 - val_loss: 1.0612
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0200 - val_loss: 1.0635
Epoch 58/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0306 - val_loss: 1.0479
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0403 - val_loss: 1.0776
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0173 - val_loss: 1.0830
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0216 - val_loss: 1.0526
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0068 - val_loss: 1.0671
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0068 - val_loss: 1.0450
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0045 - val_loss: 1.0750
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0226 - val_loss: 1.0453
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0088 - val_loss: 1.0605
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0109 - val_loss: 1.0522
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0011 - val_loss: 1.0471
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0116 - val_loss: 1.0519
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0127 - val_loss: 1.0517
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0095 - val_loss: 1.0511
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9976 - val_loss: 1.0556
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0034 - val_loss: 1.0505
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9984 - val_loss: 1.0634
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0281 - val_loss: 1.0610
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9934 - val_loss: 1.0419
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9945 - val_loss: 1.0354
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0022 - val_loss: 1.0409
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0024 - val_loss: 1.0430
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9927 - val_loss: 1.0586
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0016 - val_loss: 1.0334
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0058 - val_loss: 1.0468
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9892 - val_loss: 1.0343
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9910 - val_loss: 1.0416
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9977 - val_loss: 1.0489
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9977 - val_loss: 1.0639
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9819 - val_loss: 1.0361
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9868 - val_loss: 1.0471
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9906 - val_loss: 1.0289
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9935 - val_loss: 1.0578
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9807 - val_loss: 1.0439
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9821 - val_loss: 1.0335
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9814 - val_loss: 1.0406
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9817 - val_loss: 1.0385
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9846 - val_loss: 1.0334
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9763 - val_loss: 1.0510
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9820 - val_loss: 1.0489
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9968 - val_loss: 1.0394
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9791 - val_loss: 1.0572
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9910 - val_loss: 1.0370
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.33 seconds.
    Calculating affinities...
    Calculated affinities in 0.02 seconds.
  Calculated graph and diffusion operator in 0.36 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 4.12 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.32 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 9.57 seconds.
Calculated PHATE in 14.39 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_60 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_61 (Dense)                (None, 32)           2080        dense_60[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_61[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_61[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_62 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_63 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_64 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_60 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_61 (Dense)                (None, 32)           2080        dense_60[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_61[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_61[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_200 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_200 ( [()]                 0           tf_op_layer_Shape_200[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_20 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_200[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_20[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_60 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_20 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_60[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_201 (TensorFl [(2,)]               0           tf_op_layer_Add_20[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_203 (TensorFl [(2,)]               0           tf_op_layer_Add_20[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_202 (TensorFl [(2,)]               0           tf_op_layer_Add_20[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_204 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_206 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_205 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_207 (TensorFl [(2,)]               0           tf_op_layer_Add_20[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_209 (TensorFl [(2,)]               0           tf_op_layer_Add_20[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_208 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_201 ( [()]                 0           tf_op_layer_Shape_201[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_203 ( [()]                 0           tf_op_layer_Shape_203[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_202 ( [()]                 0           tf_op_layer_Shape_202[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_204 ( [()]                 0           tf_op_layer_Shape_204[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_206 ( [()]                 0           tf_op_layer_Shape_206[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_205 ( [()]                 0           tf_op_layer_Shape_205[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_207 ( [()]                 0           tf_op_layer_Shape_207[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_209 ( [()]                 0           tf_op_layer_Shape_209[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_208 ( [()]                 0           tf_op_layer_Shape_208[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_120/shape ( [(3,)]               0           tf_op_layer_strided_slice_201[0][
                                                                 tf_op_layer_strided_slice_203[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_121/shape ( [(3,)]               0           tf_op_layer_strided_slice_202[0][
                                                                 tf_op_layer_strided_slice_203[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_122/shape ( [(3,)]               0           tf_op_layer_strided_slice_204[0][
                                                                 tf_op_layer_strided_slice_206[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_123/shape ( [(3,)]               0           tf_op_layer_strided_slice_205[0][
                                                                 tf_op_layer_strided_slice_206[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_124/shape ( [(3,)]               0           tf_op_layer_strided_slice_207[0][
                                                                 tf_op_layer_strided_slice_209[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_125/shape ( [(3,)]               0           tf_op_layer_strided_slice_208[0][
                                                                 tf_op_layer_strided_slice_209[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_120 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_20[0][0]         
                                                                 tf_op_layer_Reshape_120/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_120/multiples  [(3,)]               0           tf_op_layer_strided_slice_202[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_121 (Tensor [(1, None, None)]    0           tf_op_layer_Add_20[0][0]         
                                                                 tf_op_layer_Reshape_121/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_121/multiples  [(3,)]               0           tf_op_layer_strided_slice_201[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_122 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_122/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_122/multiples  [(3,)]               0           tf_op_layer_strided_slice_205[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_123 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_123/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_123/multiples  [(3,)]               0           tf_op_layer_strided_slice_204[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_124 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_20[0][0]         
                                                                 tf_op_layer_Reshape_124/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_124/multiples  [(3,)]               0           tf_op_layer_strided_slice_208[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_125 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_125/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_125/multiples  [(3,)]               0           tf_op_layer_strided_slice_207[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_120 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_120[0][0]    
                                                                 tf_op_layer_Tile_120/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_121 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_121[0][0]    
                                                                 tf_op_layer_Tile_121/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_122 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_122[0][0]    
                                                                 tf_op_layer_Tile_122/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_123 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_123[0][0]    
                                                                 tf_op_layer_Tile_123/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_124 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_124[0][0]    
                                                                 tf_op_layer_Tile_124/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_125 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_125[0][0]    
                                                                 tf_op_layer_Tile_125/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_100 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_120[0][0]       
                                                                 tf_op_layer_Tile_121[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_101 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_122[0][0]       
                                                                 tf_op_layer_Tile_123[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_102 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_124[0][0]       
                                                                 tf_op_layer_Tile_125[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_80 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_100[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_81 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_101[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_82 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_102[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_140 (TensorFlo [(None, None)]       0           tf_op_layer_Square_80[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_141 (TensorFlo [(None, None)]       0           tf_op_layer_Square_81[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_142 (TensorFlo [(None, None)]       0           tf_op_layer_Square_82[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_60 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_140[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_60 (TensorFlow [()]                 0           tf_op_layer_strided_slice_203[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_61 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_141[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_61 (TensorFlow [()]                 0           tf_op_layer_strided_slice_206[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_62 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_142[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_62 (TensorFlow [()]                 0           tf_op_layer_strided_slice_209[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_60 (TensorF [(None, None)]       0           tf_op_layer_Neg_60[0][0]         
                                                                 tf_op_layer_Cast_60[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_61 (TensorF [(None, None)]       0           tf_op_layer_Neg_61[0][0]         
                                                                 tf_op_layer_Cast_61[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_62 (TensorF [(None, None)]       0           tf_op_layer_Neg_62[0][0]         
                                                                 tf_op_layer_Cast_62[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_60 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_60[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_61 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_61[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_62 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_62[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_143 (TensorFlo [()]                 0           tf_op_layer_Exp_60[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_144 (TensorFlo [()]                 0           tf_op_layer_Exp_61[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_145 (TensorFlo [()]                 0           tf_op_layer_Exp_62[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_104 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_40 (TensorFlo [()]                 0           tf_op_layer_Mean_143[0][0]       
                                                                 tf_op_layer_Mean_144[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_61 (TensorFlowO [()]                 0           tf_op_layer_Mean_145[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_83 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_104[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_103 (TensorFlow [()]                 0           tf_op_layer_AddV2_40[0][0]       
                                                                 tf_op_layer_Mul_61[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_20 (TensorFlowO [(None,)]            0           tf_op_layer_Square_83[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_62 (TensorFlowO [()]                 0           tf_op_layer_Sub_103[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_41 (TensorFlo [(None,)]            0           tf_op_layer_Sum_20[0][0]         
                                                                 tf_op_layer_Mul_62[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_146 (TensorFlo [()]                 0           tf_op_layer_AddV2_41[0][0]       
__________________________________________________________________________________________________
add_loss_20 (AddLoss)           ()                   0           tf_op_layer_Mean_146[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 9.4509WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0095s vs `on_train_batch_end` time: 0.2575s). Check your callbacks.
24/24 [==============================] - 1s 35ms/step - loss: 3.9381 - val_loss: 1.7589
Epoch 2/100
24/24 [==============================] - 0s 4ms/step - loss: 1.4592 - val_loss: 1.3306
Epoch 3/100
24/24 [==============================] - 0s 4ms/step - loss: 1.2595 - val_loss: 1.1974
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2007 - val_loss: 1.1330
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1522 - val_loss: 1.0719
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1014 - val_loss: 1.0427
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0840 - val_loss: 0.9941
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0374 - val_loss: 0.9756
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0198 - val_loss: 0.9630
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9493 - val_loss: 0.9358
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9184 - val_loss: 0.9363
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8928 - val_loss: 0.8723
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8570 - val_loss: 0.8655
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8374 - val_loss: 0.8524
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8288 - val_loss: 0.8232
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8101 - val_loss: 0.8194
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8036 - val_loss: 0.8218
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7873 - val_loss: 0.7993
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7853 - val_loss: 0.7926
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7788 - val_loss: 0.8178
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7719 - val_loss: 0.8056
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7833 - val_loss: 0.7962
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7630 - val_loss: 0.7797
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7604 - val_loss: 0.7764
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7682 - val_loss: 0.7904
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7580 - val_loss: 0.7760
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7581 - val_loss: 0.7614
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7444 - val_loss: 0.7678
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7455 - val_loss: 0.7733
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7485 - val_loss: 0.7922
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7479 - val_loss: 0.7745
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7423 - val_loss: 0.7853
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7320 - val_loss: 0.7584
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7383 - val_loss: 0.7813
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7341 - val_loss: 0.7723
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7337 - val_loss: 0.7580
Epoch 37/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7312 - val_loss: 0.7573
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7241 - val_loss: 0.7511
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7298 - val_loss: 0.7478
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7256 - val_loss: 0.7510
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7173 - val_loss: 0.7523
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7239 - val_loss: 0.7621
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7262 - val_loss: 0.7822
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7246 - val_loss: 0.7624
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7147 - val_loss: 0.7528
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7150 - val_loss: 0.7513
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7150 - val_loss: 0.7475
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7158 - val_loss: 0.7432
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7056 - val_loss: 0.7575
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7044 - val_loss: 0.7485
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7099 - val_loss: 0.7498
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7126 - val_loss: 0.7398
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6985 - val_loss: 0.7527
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7027 - val_loss: 0.7812
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7079 - val_loss: 0.7500
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7088 - val_loss: 0.7513
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7010 - val_loss: 0.7418
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7056 - val_loss: 0.7621
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6912 - val_loss: 0.7496
Epoch 60/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6954 - val_loss: 0.7537
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7031 - val_loss: 0.7488
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6980 - val_loss: 0.7584
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6986 - val_loss: 0.7511
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6857 - val_loss: 0.7640
Epoch 65/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6861 - val_loss: 0.7421
Epoch 66/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6955 - val_loss: 0.7397
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6939 - val_loss: 0.7470
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6823 - val_loss: 0.7481
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6894 - val_loss: 0.7603
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6891 - val_loss: 0.7509
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6860 - val_loss: 0.7676
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6844 - val_loss: 0.7541
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6887 - val_loss: 0.7454
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6834 - val_loss: 0.7461
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6865 - val_loss: 0.7536
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6892 - val_loss: 0.7403
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6879 - val_loss: 0.7766
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6816 - val_loss: 0.7618
Epoch 79/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6950 - val_loss: 0.7271
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6893 - val_loss: 0.7431
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6811 - val_loss: 0.7552
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6814 - val_loss: 0.7607
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6823 - val_loss: 0.7358
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6825 - val_loss: 0.7512
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6830 - val_loss: 0.7378
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6859 - val_loss: 0.7429
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6772 - val_loss: 0.7472
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6821 - val_loss: 0.7472
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6833 - val_loss: 0.7380
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6804 - val_loss: 0.7343
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6779 - val_loss: 0.7458
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6766 - val_loss: 0.7434
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6779 - val_loss: 0.7315
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6819 - val_loss: 0.7271
Epoch 95/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6729 - val_loss: 0.7506
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6739 - val_loss: 0.7369
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6828 - val_loss: 0.7382
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6842 - val_loss: 0.7282
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6725 - val_loss: 0.7323
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6694 - val_loss: 0.7367
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_65 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_210 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_210 ( [()]                 0           tf_op_layer_Shape_210[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_21 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_210[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_21[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_63 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_21 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_63[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_211 (TensorFl [(2,)]               0           tf_op_layer_Add_21[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_213 (TensorFl [(2,)]               0           tf_op_layer_Add_21[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_212 (TensorFl [(2,)]               0           tf_op_layer_Add_21[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_214 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_216 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_215 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_217 (TensorFl [(2,)]               0           tf_op_layer_Add_21[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_219 (TensorFl [(2,)]               0           tf_op_layer_Add_21[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_218 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_211 ( [()]                 0           tf_op_layer_Shape_211[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_213 ( [()]                 0           tf_op_layer_Shape_213[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_212 ( [()]                 0           tf_op_layer_Shape_212[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_214 ( [()]                 0           tf_op_layer_Shape_214[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_216 ( [()]                 0           tf_op_layer_Shape_216[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_215 ( [()]                 0           tf_op_layer_Shape_215[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_217 ( [()]                 0           tf_op_layer_Shape_217[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_219 ( [()]                 0           tf_op_layer_Shape_219[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_218 ( [()]                 0           tf_op_layer_Shape_218[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_126/shape ( [(3,)]               0           tf_op_layer_strided_slice_211[0][
                                                                 tf_op_layer_strided_slice_213[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_127/shape ( [(3,)]               0           tf_op_layer_strided_slice_212[0][
                                                                 tf_op_layer_strided_slice_213[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_128/shape ( [(3,)]               0           tf_op_layer_strided_slice_214[0][
                                                                 tf_op_layer_strided_slice_216[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_129/shape ( [(3,)]               0           tf_op_layer_strided_slice_215[0][
                                                                 tf_op_layer_strided_slice_216[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_130/shape ( [(3,)]               0           tf_op_layer_strided_slice_217[0][
                                                                 tf_op_layer_strided_slice_219[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_131/shape ( [(3,)]               0           tf_op_layer_strided_slice_218[0][
                                                                 tf_op_layer_strided_slice_219[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_126 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_21[0][0]         
                                                                 tf_op_layer_Reshape_126/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_126/multiples  [(3,)]               0           tf_op_layer_strided_slice_212[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_127 (Tensor [(1, None, None)]    0           tf_op_layer_Add_21[0][0]         
                                                                 tf_op_layer_Reshape_127/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_127/multiples  [(3,)]               0           tf_op_layer_strided_slice_211[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_128 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_128/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_128/multiples  [(3,)]               0           tf_op_layer_strided_slice_215[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_129 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_129/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_129/multiples  [(3,)]               0           tf_op_layer_strided_slice_214[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_130 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_21[0][0]         
                                                                 tf_op_layer_Reshape_130/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_130/multiples  [(3,)]               0           tf_op_layer_strided_slice_218[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_131 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_131/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_131/multiples  [(3,)]               0           tf_op_layer_strided_slice_217[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_126 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_126[0][0]    
                                                                 tf_op_layer_Tile_126/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_127 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_127[0][0]    
                                                                 tf_op_layer_Tile_127/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_128 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_128[0][0]    
                                                                 tf_op_layer_Tile_128/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_129 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_129[0][0]    
                                                                 tf_op_layer_Tile_129/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_130 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_130[0][0]    
                                                                 tf_op_layer_Tile_130/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_131 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_131[0][0]    
                                                                 tf_op_layer_Tile_131/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_105 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_126[0][0]       
                                                                 tf_op_layer_Tile_127[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_106 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_128[0][0]       
                                                                 tf_op_layer_Tile_129[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_107 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_130[0][0]       
                                                                 tf_op_layer_Tile_131[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_84 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_105[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_85 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_106[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_86 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_107[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_147 (TensorFlo [(None, None)]       0           tf_op_layer_Square_84[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_148 (TensorFlo [(None, None)]       0           tf_op_layer_Square_85[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_149 (TensorFlo [(None, None)]       0           tf_op_layer_Square_86[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_63 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_147[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_63 (TensorFlow [()]                 0           tf_op_layer_strided_slice_213[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_64 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_148[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_64 (TensorFlow [()]                 0           tf_op_layer_strided_slice_216[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_65 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_149[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_65 (TensorFlow [()]                 0           tf_op_layer_strided_slice_219[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_63 (TensorF [(None, None)]       0           tf_op_layer_Neg_63[0][0]         
                                                                 tf_op_layer_Cast_63[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_64 (TensorF [(None, None)]       0           tf_op_layer_Neg_64[0][0]         
                                                                 tf_op_layer_Cast_64[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_65 (TensorF [(None, None)]       0           tf_op_layer_Neg_65[0][0]         
                                                                 tf_op_layer_Cast_65[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_63 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_63[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_64 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_64[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_65 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_65[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_150 (TensorFlo [()]                 0           tf_op_layer_Exp_63[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_151 (TensorFlo [()]                 0           tf_op_layer_Exp_64[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_152 (TensorFlo [()]                 0           tf_op_layer_Exp_65[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_109 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_42 (TensorFlo [()]                 0           tf_op_layer_Mean_150[0][0]       
                                                                 tf_op_layer_Mean_151[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_64 (TensorFlowO [()]                 0           tf_op_layer_Mean_152[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_87 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_109[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_108 (TensorFlow [()]                 0           tf_op_layer_AddV2_42[0][0]       
                                                                 tf_op_layer_Mul_64[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_21 (TensorFlowO [(None,)]            0           tf_op_layer_Square_87[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_65 (TensorFlowO [()]                 0           tf_op_layer_Sub_108[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_43 (TensorFlo [(None,)]            0           tf_op_layer_Sum_21[0][0]         
                                                                 tf_op_layer_Mul_65[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_153 (TensorFlo [()]                 0           tf_op_layer_AddV2_43[0][0]       
__________________________________________________________________________________________________
add_loss_21 (AddLoss)           ()                   0           tf_op_layer_Mean_153[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.2173WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.2285s). Check your callbacks.
24/24 [==============================] - 1s 26ms/step - loss: 7.7591 - val_loss: 5.8102
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 4.3905 - val_loss: 3.7928
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 2.7939 - val_loss: 2.4184
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.0744 - val_loss: 1.9968
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8338 - val_loss: 1.8143
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6802 - val_loss: 1.6850
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5799 - val_loss: 1.5976
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4542 - val_loss: 1.5809
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3785 - val_loss: 1.4824
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3144 - val_loss: 1.3977
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2775 - val_loss: 1.4176
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2455 - val_loss: 1.3532
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2334 - val_loss: 1.3944
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1786 - val_loss: 1.3175
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1691 - val_loss: 1.3448
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1590 - val_loss: 1.2724
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1273 - val_loss: 1.3026
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1258 - val_loss: 1.2552
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1202 - val_loss: 1.3152
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1104 - val_loss: 1.2379
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1189 - val_loss: 1.2531
Epoch 22/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1031 - val_loss: 1.2596
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0926 - val_loss: 1.2589
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0882 - val_loss: 1.2241
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0775 - val_loss: 1.2318
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0952 - val_loss: 1.2111
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0812 - val_loss: 1.2089
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0835 - val_loss: 1.2007
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0637 - val_loss: 1.1866
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0480 - val_loss: 1.2213
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0755 - val_loss: 1.1372
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0619 - val_loss: 1.1741
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0575 - val_loss: 1.1711
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0745 - val_loss: 1.2126
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0556 - val_loss: 1.1854
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0280 - val_loss: 1.1676
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0378 - val_loss: 1.1578
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0388 - val_loss: 1.1492
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0357 - val_loss: 1.1690
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0534 - val_loss: 1.1946
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0342 - val_loss: 1.1680
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0187 - val_loss: 1.1613
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0431 - val_loss: 1.1805
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0202 - val_loss: 1.1705
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0111 - val_loss: 1.1417
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0331 - val_loss: 1.1795
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0234 - val_loss: 1.1306
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0086 - val_loss: 1.1470
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0181 - val_loss: 1.1546
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0156 - val_loss: 1.1361
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0162 - val_loss: 1.1387
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0093 - val_loss: 1.1200
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0008 - val_loss: 1.1584
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0076 - val_loss: 1.1382
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0056 - val_loss: 1.1697
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0188 - val_loss: 1.1322
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0139 - val_loss: 1.1290
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9922 - val_loss: 1.1536
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0131 - val_loss: 1.1698
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9908 - val_loss: 1.1845
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9928 - val_loss: 1.1219
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9871 - val_loss: 1.1559
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9899 - val_loss: 1.1498
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0097 - val_loss: 1.1318
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9816 - val_loss: 1.1437
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9852 - val_loss: 1.1441
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0107 - val_loss: 1.1011
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9933 - val_loss: 1.1124
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9867 - val_loss: 1.1053
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9997 - val_loss: 1.1683
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9984 - val_loss: 1.1360
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9881 - val_loss: 1.1412
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9758 - val_loss: 1.1240
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9785 - val_loss: 1.1095
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9897 - val_loss: 1.1318
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0050 - val_loss: 1.1150
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9809 - val_loss: 1.1015
Epoch 78/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9869 - val_loss: 1.1299
Epoch 79/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9779 - val_loss: 1.0980
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9983 - val_loss: 1.1515
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9787 - val_loss: 1.0904
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9819 - val_loss: 1.0954
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9779 - val_loss: 1.1002
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9768 - val_loss: 1.1379
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9705 - val_loss: 1.0817
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9849 - val_loss: 1.0918
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9841 - val_loss: 1.1110
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9794 - val_loss: 1.1018
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9774 - val_loss: 1.1462
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9788 - val_loss: 1.0964
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9681 - val_loss: 1.1084
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0050 - val_loss: 1.1505
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9714 - val_loss: 1.1075
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9787 - val_loss: 1.1124
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9725 - val_loss: 1.1094
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9807 - val_loss: 1.0944
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9636 - val_loss: 1.0957
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9703 - val_loss: 1.0961
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9764 - val_loss: 1.0908
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9714 - val_loss: 1.1202
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.29 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.31 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.38 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.23 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 7.86 seconds.
Calculated PHATE in 9.80 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_66 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_67 (Dense)                (None, 32)           2080        dense_66[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_67[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_67[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_68 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_69 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_70 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_66 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_67 (Dense)                (None, 32)           2080        dense_66[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_67[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_67[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_220 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_220 ( [()]                 0           tf_op_layer_Shape_220[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_22 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_220[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_22[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_66 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_22 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_66[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_221 (TensorFl [(2,)]               0           tf_op_layer_Add_22[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_223 (TensorFl [(2,)]               0           tf_op_layer_Add_22[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_222 (TensorFl [(2,)]               0           tf_op_layer_Add_22[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_224 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_226 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_225 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_227 (TensorFl [(2,)]               0           tf_op_layer_Add_22[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_229 (TensorFl [(2,)]               0           tf_op_layer_Add_22[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_228 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_221 ( [()]                 0           tf_op_layer_Shape_221[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_223 ( [()]                 0           tf_op_layer_Shape_223[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_222 ( [()]                 0           tf_op_layer_Shape_222[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_224 ( [()]                 0           tf_op_layer_Shape_224[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_226 ( [()]                 0           tf_op_layer_Shape_226[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_225 ( [()]                 0           tf_op_layer_Shape_225[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_227 ( [()]                 0           tf_op_layer_Shape_227[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_229 ( [()]                 0           tf_op_layer_Shape_229[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_228 ( [()]                 0           tf_op_layer_Shape_228[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_132/shape ( [(3,)]               0           tf_op_layer_strided_slice_221[0][
                                                                 tf_op_layer_strided_slice_223[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_133/shape ( [(3,)]               0           tf_op_layer_strided_slice_222[0][
                                                                 tf_op_layer_strided_slice_223[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_134/shape ( [(3,)]               0           tf_op_layer_strided_slice_224[0][
                                                                 tf_op_layer_strided_slice_226[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_135/shape ( [(3,)]               0           tf_op_layer_strided_slice_225[0][
                                                                 tf_op_layer_strided_slice_226[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_136/shape ( [(3,)]               0           tf_op_layer_strided_slice_227[0][
                                                                 tf_op_layer_strided_slice_229[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_137/shape ( [(3,)]               0           tf_op_layer_strided_slice_228[0][
                                                                 tf_op_layer_strided_slice_229[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_132 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_22[0][0]         
                                                                 tf_op_layer_Reshape_132/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_132/multiples  [(3,)]               0           tf_op_layer_strided_slice_222[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_133 (Tensor [(1, None, None)]    0           tf_op_layer_Add_22[0][0]         
                                                                 tf_op_layer_Reshape_133/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_133/multiples  [(3,)]               0           tf_op_layer_strided_slice_221[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_134 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_134/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_134/multiples  [(3,)]               0           tf_op_layer_strided_slice_225[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_135 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_135/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_135/multiples  [(3,)]               0           tf_op_layer_strided_slice_224[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_136 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_22[0][0]         
                                                                 tf_op_layer_Reshape_136/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_136/multiples  [(3,)]               0           tf_op_layer_strided_slice_228[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_137 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_137/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_137/multiples  [(3,)]               0           tf_op_layer_strided_slice_227[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_132 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_132[0][0]    
                                                                 tf_op_layer_Tile_132/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_133 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_133[0][0]    
                                                                 tf_op_layer_Tile_133/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_134 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_134[0][0]    
                                                                 tf_op_layer_Tile_134/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_135 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_135[0][0]    
                                                                 tf_op_layer_Tile_135/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_136 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_136[0][0]    
                                                                 tf_op_layer_Tile_136/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_137 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_137[0][0]    
                                                                 tf_op_layer_Tile_137/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_110 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_132[0][0]       
                                                                 tf_op_layer_Tile_133[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_111 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_134[0][0]       
                                                                 tf_op_layer_Tile_135[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_112 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_136[0][0]       
                                                                 tf_op_layer_Tile_137[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_88 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_110[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_89 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_111[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_90 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_112[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_154 (TensorFlo [(None, None)]       0           tf_op_layer_Square_88[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_155 (TensorFlo [(None, None)]       0           tf_op_layer_Square_89[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_156 (TensorFlo [(None, None)]       0           tf_op_layer_Square_90[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_66 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_154[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_66 (TensorFlow [()]                 0           tf_op_layer_strided_slice_223[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_67 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_155[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_67 (TensorFlow [()]                 0           tf_op_layer_strided_slice_226[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_68 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_156[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_68 (TensorFlow [()]                 0           tf_op_layer_strided_slice_229[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_66 (TensorF [(None, None)]       0           tf_op_layer_Neg_66[0][0]         
                                                                 tf_op_layer_Cast_66[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_67 (TensorF [(None, None)]       0           tf_op_layer_Neg_67[0][0]         
                                                                 tf_op_layer_Cast_67[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_68 (TensorF [(None, None)]       0           tf_op_layer_Neg_68[0][0]         
                                                                 tf_op_layer_Cast_68[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_66 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_66[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_67 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_67[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_68 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_68[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_157 (TensorFlo [()]                 0           tf_op_layer_Exp_66[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_158 (TensorFlo [()]                 0           tf_op_layer_Exp_67[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_159 (TensorFlo [()]                 0           tf_op_layer_Exp_68[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_114 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_44 (TensorFlo [()]                 0           tf_op_layer_Mean_157[0][0]       
                                                                 tf_op_layer_Mean_158[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_67 (TensorFlowO [()]                 0           tf_op_layer_Mean_159[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_91 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_114[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_113 (TensorFlow [()]                 0           tf_op_layer_AddV2_44[0][0]       
                                                                 tf_op_layer_Mul_67[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_22 (TensorFlowO [(None,)]            0           tf_op_layer_Square_91[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_68 (TensorFlowO [()]                 0           tf_op_layer_Sub_113[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_45 (TensorFlo [(None,)]            0           tf_op_layer_Sum_22[0][0]         
                                                                 tf_op_layer_Mul_68[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_160 (TensorFlo [()]                 0           tf_op_layer_AddV2_45[0][0]       
__________________________________________________________________________________________________
add_loss_22 (AddLoss)           ()                   0           tf_op_layer_Mean_160[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.0370WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.1305s). Check your callbacks.
24/24 [==============================] - 0s 19ms/step - loss: 4.2315 - val_loss: 1.9872
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5414 - val_loss: 1.4678
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2176 - val_loss: 1.3352
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0999 - val_loss: 1.2624
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9654 - val_loss: 1.1227
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9160 - val_loss: 1.0396
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8752 - val_loss: 1.0214
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8598 - val_loss: 0.9762
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8442 - val_loss: 1.0214
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8338 - val_loss: 1.0241
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8306 - val_loss: 1.0008
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8008 - val_loss: 0.9773
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8083 - val_loss: 0.9576
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7986 - val_loss: 0.9692
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8043 - val_loss: 0.9322
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7954 - val_loss: 0.9728
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7936 - val_loss: 0.9975
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7794 - val_loss: 0.9399
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7833 - val_loss: 0.9625
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7832 - val_loss: 0.9395
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8064 - val_loss: 0.9831
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7763 - val_loss: 0.9425
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7664 - val_loss: 0.9618
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7770 - val_loss: 0.9367
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7643 - val_loss: 0.9053
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7639 - val_loss: 0.9186
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7645 - val_loss: 0.9381
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7619 - val_loss: 0.9149
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7827 - val_loss: 0.9479
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7622 - val_loss: 0.9289
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7483 - val_loss: 0.9434
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7605 - val_loss: 0.8992
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7454 - val_loss: 0.9001
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7455 - val_loss: 0.9168
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7372 - val_loss: 0.9007
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7412 - val_loss: 0.9013
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7423 - val_loss: 0.8978
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7409 - val_loss: 0.8999
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7429 - val_loss: 0.9462
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7407 - val_loss: 0.9469
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7362 - val_loss: 0.8953
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7335 - val_loss: 0.8959
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7253 - val_loss: 0.8963
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7202 - val_loss: 0.9101
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7265 - val_loss: 0.9193
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7361 - val_loss: 0.9258
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7288 - val_loss: 0.8836
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7312 - val_loss: 0.8669
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7263 - val_loss: 0.9315
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7161 - val_loss: 0.9125
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7170 - val_loss: 0.8878
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7145 - val_loss: 0.9068
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7115 - val_loss: 0.9146
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7197 - val_loss: 0.8875
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7223 - val_loss: 0.8895
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7082 - val_loss: 0.8885
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7223 - val_loss: 0.8837
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7153 - val_loss: 0.8725
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7129 - val_loss: 0.9262
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7137 - val_loss: 0.9318
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7256 - val_loss: 0.8609
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7085 - val_loss: 0.8758
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7151 - val_loss: 0.9043
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7137 - val_loss: 0.8687
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7049 - val_loss: 0.9446
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7051 - val_loss: 0.9020
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7051 - val_loss: 0.9259
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7148 - val_loss: 0.8798
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7113 - val_loss: 0.9051
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7123 - val_loss: 0.8980
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7062 - val_loss: 0.8701
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6972 - val_loss: 0.8774
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6982 - val_loss: 0.8741
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6971 - val_loss: 0.8620
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6968 - val_loss: 0.8573
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7020 - val_loss: 0.8679
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7104 - val_loss: 0.8856
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6974 - val_loss: 0.8557
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7010 - val_loss: 0.8922
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6917 - val_loss: 0.9058
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7015 - val_loss: 0.8808
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6952 - val_loss: 0.8713
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6873 - val_loss: 0.8753
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7047 - val_loss: 0.9068
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6928 - val_loss: 0.8999
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6912 - val_loss: 0.8481
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6901 - val_loss: 0.8784
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6985 - val_loss: 0.8386
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6907 - val_loss: 0.8944
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6950 - val_loss: 0.8569
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6917 - val_loss: 0.8582
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6851 - val_loss: 0.8476
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6886 - val_loss: 0.8682
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6953 - val_loss: 0.8859
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6902 - val_loss: 0.8427
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6972 - val_loss: 0.8515
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6922 - val_loss: 0.8777
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6789 - val_loss: 0.8817
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6846 - val_loss: 0.8695
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6819 - val_loss: 0.8493
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_71 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_230 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_230 ( [()]                 0           tf_op_layer_Shape_230[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_23 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_230[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_23[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_69 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_23 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_69[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_231 (TensorFl [(2,)]               0           tf_op_layer_Add_23[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_233 (TensorFl [(2,)]               0           tf_op_layer_Add_23[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_232 (TensorFl [(2,)]               0           tf_op_layer_Add_23[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_234 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_236 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_235 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_237 (TensorFl [(2,)]               0           tf_op_layer_Add_23[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_239 (TensorFl [(2,)]               0           tf_op_layer_Add_23[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_238 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_231 ( [()]                 0           tf_op_layer_Shape_231[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_233 ( [()]                 0           tf_op_layer_Shape_233[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_232 ( [()]                 0           tf_op_layer_Shape_232[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_234 ( [()]                 0           tf_op_layer_Shape_234[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_236 ( [()]                 0           tf_op_layer_Shape_236[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_235 ( [()]                 0           tf_op_layer_Shape_235[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_237 ( [()]                 0           tf_op_layer_Shape_237[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_239 ( [()]                 0           tf_op_layer_Shape_239[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_238 ( [()]                 0           tf_op_layer_Shape_238[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_138/shape ( [(3,)]               0           tf_op_layer_strided_slice_231[0][
                                                                 tf_op_layer_strided_slice_233[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_139/shape ( [(3,)]               0           tf_op_layer_strided_slice_232[0][
                                                                 tf_op_layer_strided_slice_233[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_140/shape ( [(3,)]               0           tf_op_layer_strided_slice_234[0][
                                                                 tf_op_layer_strided_slice_236[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_141/shape ( [(3,)]               0           tf_op_layer_strided_slice_235[0][
                                                                 tf_op_layer_strided_slice_236[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_142/shape ( [(3,)]               0           tf_op_layer_strided_slice_237[0][
                                                                 tf_op_layer_strided_slice_239[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_143/shape ( [(3,)]               0           tf_op_layer_strided_slice_238[0][
                                                                 tf_op_layer_strided_slice_239[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_138 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_23[0][0]         
                                                                 tf_op_layer_Reshape_138/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_138/multiples  [(3,)]               0           tf_op_layer_strided_slice_232[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_139 (Tensor [(1, None, None)]    0           tf_op_layer_Add_23[0][0]         
                                                                 tf_op_layer_Reshape_139/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_139/multiples  [(3,)]               0           tf_op_layer_strided_slice_231[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_140 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_140/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_140/multiples  [(3,)]               0           tf_op_layer_strided_slice_235[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_141 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_141/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_141/multiples  [(3,)]               0           tf_op_layer_strided_slice_234[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_142 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_23[0][0]         
                                                                 tf_op_layer_Reshape_142/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_142/multiples  [(3,)]               0           tf_op_layer_strided_slice_238[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_143 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_143/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_143/multiples  [(3,)]               0           tf_op_layer_strided_slice_237[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_138 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_138[0][0]    
                                                                 tf_op_layer_Tile_138/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_139 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_139[0][0]    
                                                                 tf_op_layer_Tile_139/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_140 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_140[0][0]    
                                                                 tf_op_layer_Tile_140/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_141 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_141[0][0]    
                                                                 tf_op_layer_Tile_141/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_142 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_142[0][0]    
                                                                 tf_op_layer_Tile_142/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_143 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_143[0][0]    
                                                                 tf_op_layer_Tile_143/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_115 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_138[0][0]       
                                                                 tf_op_layer_Tile_139[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_116 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_140[0][0]       
                                                                 tf_op_layer_Tile_141[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_117 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_142[0][0]       
                                                                 tf_op_layer_Tile_143[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_92 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_115[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_93 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_116[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_94 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_117[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_161 (TensorFlo [(None, None)]       0           tf_op_layer_Square_92[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_162 (TensorFlo [(None, None)]       0           tf_op_layer_Square_93[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_163 (TensorFlo [(None, None)]       0           tf_op_layer_Square_94[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_69 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_161[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_69 (TensorFlow [()]                 0           tf_op_layer_strided_slice_233[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_70 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_162[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_70 (TensorFlow [()]                 0           tf_op_layer_strided_slice_236[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_71 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_163[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_71 (TensorFlow [()]                 0           tf_op_layer_strided_slice_239[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_69 (TensorF [(None, None)]       0           tf_op_layer_Neg_69[0][0]         
                                                                 tf_op_layer_Cast_69[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_70 (TensorF [(None, None)]       0           tf_op_layer_Neg_70[0][0]         
                                                                 tf_op_layer_Cast_70[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_71 (TensorF [(None, None)]       0           tf_op_layer_Neg_71[0][0]         
                                                                 tf_op_layer_Cast_71[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_69 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_69[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_70 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_70[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_71 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_71[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_164 (TensorFlo [()]                 0           tf_op_layer_Exp_69[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_165 (TensorFlo [()]                 0           tf_op_layer_Exp_70[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_166 (TensorFlo [()]                 0           tf_op_layer_Exp_71[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_119 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_46 (TensorFlo [()]                 0           tf_op_layer_Mean_164[0][0]       
                                                                 tf_op_layer_Mean_165[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_70 (TensorFlowO [()]                 0           tf_op_layer_Mean_166[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_95 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_119[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_118 (TensorFlow [()]                 0           tf_op_layer_AddV2_46[0][0]       
                                                                 tf_op_layer_Mul_70[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_23 (TensorFlowO [(None,)]            0           tf_op_layer_Square_95[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_71 (TensorFlowO [()]                 0           tf_op_layer_Sub_118[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_47 (TensorFlo [(None,)]            0           tf_op_layer_Sum_23[0][0]         
                                                                 tf_op_layer_Mul_71[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_167 (TensorFlo [()]                 0           tf_op_layer_AddV2_47[0][0]       
__________________________________________________________________________________________________
add_loss_23 (AddLoss)           ()                   0           tf_op_layer_Mean_167[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.7230WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1938s). Check your callbacks.
24/24 [==============================] - 1s 22ms/step - loss: 8.6558 - val_loss: 6.1472
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.6955 - val_loss: 3.5593
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.9088 - val_loss: 2.2550
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.1078 - val_loss: 1.8404
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8627 - val_loss: 1.7382
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.7203 - val_loss: 1.6088
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6418 - val_loss: 1.5817
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6095 - val_loss: 1.5219
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5445 - val_loss: 1.5113
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5172 - val_loss: 1.4457
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5072 - val_loss: 1.4090
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4605 - val_loss: 1.4211
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4330 - val_loss: 1.3434
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4107 - val_loss: 1.3235
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3688 - val_loss: 1.3439
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3459 - val_loss: 1.2841
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3063 - val_loss: 1.2464
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2732 - val_loss: 1.2154
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2479 - val_loss: 1.1818
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2215 - val_loss: 1.1746
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2127 - val_loss: 1.1618
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1992 - val_loss: 1.1359
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1769 - val_loss: 1.1227
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1555 - val_loss: 1.1192
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1563 - val_loss: 1.1080
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1411 - val_loss: 1.1281
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1275 - val_loss: 1.1052
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1169 - val_loss: 1.0511
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1215 - val_loss: 1.0912
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1175 - val_loss: 1.0747
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0982 - val_loss: 1.0775
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0982 - val_loss: 1.0941
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0751 - val_loss: 1.0348
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0914 - val_loss: 1.1123
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0695 - val_loss: 1.0456
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0626 - val_loss: 1.0357
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0728 - val_loss: 1.0396
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0583 - val_loss: 1.0340
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0624 - val_loss: 1.0220
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0584 - val_loss: 1.0609
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0739 - val_loss: 1.0483
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0512 - val_loss: 1.0548
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0406 - val_loss: 0.9985
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0462 - val_loss: 1.0332
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0464 - val_loss: 1.0133
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0530 - val_loss: 1.0265
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0439 - val_loss: 1.0012
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0284 - val_loss: 1.0217
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0412 - val_loss: 1.0099
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0298 - val_loss: 1.0111
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0457 - val_loss: 1.0094
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0314 - val_loss: 0.9932
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0290 - val_loss: 1.0230
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0376 - val_loss: 1.0193
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0220 - val_loss: 0.9992
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0209 - val_loss: 1.0169
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0205 - val_loss: 1.0064
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0277 - val_loss: 0.9733
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0230 - val_loss: 0.9899
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0347 - val_loss: 0.9747
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0105 - val_loss: 0.9809
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0273 - val_loss: 0.9957
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0382 - val_loss: 1.0145
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0116 - val_loss: 0.9840
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0109 - val_loss: 0.9873
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0233 - val_loss: 0.9705
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0168 - val_loss: 0.9873
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0167 - val_loss: 1.0150
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0122 - val_loss: 0.9903
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0090 - val_loss: 0.9875
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0008 - val_loss: 0.9736
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0121 - val_loss: 0.9829
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0165 - val_loss: 0.9780
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0069 - val_loss: 0.9758
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9871 - val_loss: 0.9865
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0008 - val_loss: 0.9949
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0128 - val_loss: 0.9701
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0041 - val_loss: 0.9668
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0004 - val_loss: 0.9550
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0105 - val_loss: 0.9919
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0042 - val_loss: 0.9677
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0135 - val_loss: 0.9610
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9920 - val_loss: 0.9978
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9989 - val_loss: 0.9690
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0013 - val_loss: 0.9648
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0009 - val_loss: 0.9458
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9900 - val_loss: 0.9753
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0029 - val_loss: 0.9487
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0035 - val_loss: 0.9770
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9895 - val_loss: 1.0073
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9895 - val_loss: 0.9744
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9865 - val_loss: 0.9612
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9882 - val_loss: 0.9861
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9924 - val_loss: 0.9723
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9827 - val_loss: 0.9440
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0102 - val_loss: 0.9726
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9841 - val_loss: 0.9963
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9902 - val_loss: 0.9763
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9914 - val_loss: 0.9625
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9891 - val_loss: 0.9639
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.38 seconds.
    Calculating affinities...
    Calculated affinities in 0.02 seconds.
  Calculated graph and diffusion operator in 0.41 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 2.56 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.26 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 7.20 seconds.
Calculated PHATE in 10.45 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_72 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_73 (Dense)                (None, 32)           2080        dense_72[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_73[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_73[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_74 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_75 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_76 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_72 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_73 (Dense)                (None, 32)           2080        dense_72[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_73[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_73[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_240 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_240 ( [()]                 0           tf_op_layer_Shape_240[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_24 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_240[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_24[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_72 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_24 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_72[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_241 (TensorFl [(2,)]               0           tf_op_layer_Add_24[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_243 (TensorFl [(2,)]               0           tf_op_layer_Add_24[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_242 (TensorFl [(2,)]               0           tf_op_layer_Add_24[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_244 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_246 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_245 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_247 (TensorFl [(2,)]               0           tf_op_layer_Add_24[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_249 (TensorFl [(2,)]               0           tf_op_layer_Add_24[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_248 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_241 ( [()]                 0           tf_op_layer_Shape_241[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_243 ( [()]                 0           tf_op_layer_Shape_243[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_242 ( [()]                 0           tf_op_layer_Shape_242[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_244 ( [()]                 0           tf_op_layer_Shape_244[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_246 ( [()]                 0           tf_op_layer_Shape_246[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_245 ( [()]                 0           tf_op_layer_Shape_245[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_247 ( [()]                 0           tf_op_layer_Shape_247[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_249 ( [()]                 0           tf_op_layer_Shape_249[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_248 ( [()]                 0           tf_op_layer_Shape_248[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_144/shape ( [(3,)]               0           tf_op_layer_strided_slice_241[0][
                                                                 tf_op_layer_strided_slice_243[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_145/shape ( [(3,)]               0           tf_op_layer_strided_slice_242[0][
                                                                 tf_op_layer_strided_slice_243[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_146/shape ( [(3,)]               0           tf_op_layer_strided_slice_244[0][
                                                                 tf_op_layer_strided_slice_246[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_147/shape ( [(3,)]               0           tf_op_layer_strided_slice_245[0][
                                                                 tf_op_layer_strided_slice_246[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_148/shape ( [(3,)]               0           tf_op_layer_strided_slice_247[0][
                                                                 tf_op_layer_strided_slice_249[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_149/shape ( [(3,)]               0           tf_op_layer_strided_slice_248[0][
                                                                 tf_op_layer_strided_slice_249[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_144 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_24[0][0]         
                                                                 tf_op_layer_Reshape_144/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_144/multiples  [(3,)]               0           tf_op_layer_strided_slice_242[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_145 (Tensor [(1, None, None)]    0           tf_op_layer_Add_24[0][0]         
                                                                 tf_op_layer_Reshape_145/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_145/multiples  [(3,)]               0           tf_op_layer_strided_slice_241[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_146 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_146/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_146/multiples  [(3,)]               0           tf_op_layer_strided_slice_245[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_147 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_147/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_147/multiples  [(3,)]               0           tf_op_layer_strided_slice_244[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_148 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_24[0][0]         
                                                                 tf_op_layer_Reshape_148/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_148/multiples  [(3,)]               0           tf_op_layer_strided_slice_248[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_149 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_149/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_149/multiples  [(3,)]               0           tf_op_layer_strided_slice_247[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_144 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_144[0][0]    
                                                                 tf_op_layer_Tile_144/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_145 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_145[0][0]    
                                                                 tf_op_layer_Tile_145/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_146 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_146[0][0]    
                                                                 tf_op_layer_Tile_146/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_147 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_147[0][0]    
                                                                 tf_op_layer_Tile_147/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_148 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_148[0][0]    
                                                                 tf_op_layer_Tile_148/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_149 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_149[0][0]    
                                                                 tf_op_layer_Tile_149/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_120 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_144[0][0]       
                                                                 tf_op_layer_Tile_145[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_121 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_146[0][0]       
                                                                 tf_op_layer_Tile_147[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_122 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_148[0][0]       
                                                                 tf_op_layer_Tile_149[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_96 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_120[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_97 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_121[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_98 (TensorFl [(None, None, None)] 0           tf_op_layer_Sub_122[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_168 (TensorFlo [(None, None)]       0           tf_op_layer_Square_96[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_169 (TensorFlo [(None, None)]       0           tf_op_layer_Square_97[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mean_170 (TensorFlo [(None, None)]       0           tf_op_layer_Square_98[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Neg_72 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_168[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_72 (TensorFlow [()]                 0           tf_op_layer_strided_slice_243[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_73 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_169[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_73 (TensorFlow [()]                 0           tf_op_layer_strided_slice_246[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_74 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_170[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_74 (TensorFlow [()]                 0           tf_op_layer_strided_slice_249[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_72 (TensorF [(None, None)]       0           tf_op_layer_Neg_72[0][0]         
                                                                 tf_op_layer_Cast_72[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_73 (TensorF [(None, None)]       0           tf_op_layer_Neg_73[0][0]         
                                                                 tf_op_layer_Cast_73[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_74 (TensorF [(None, None)]       0           tf_op_layer_Neg_74[0][0]         
                                                                 tf_op_layer_Cast_74[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_72 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_72[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_73 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_73[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_74 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_74[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_171 (TensorFlo [()]                 0           tf_op_layer_Exp_72[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_172 (TensorFlo [()]                 0           tf_op_layer_Exp_73[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_173 (TensorFlo [()]                 0           tf_op_layer_Exp_74[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_124 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_48 (TensorFlo [()]                 0           tf_op_layer_Mean_171[0][0]       
                                                                 tf_op_layer_Mean_172[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_73 (TensorFlowO [()]                 0           tf_op_layer_Mean_173[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_99 (TensorFl [(None, 97)]         0           tf_op_layer_Sub_124[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_123 (TensorFlow [()]                 0           tf_op_layer_AddV2_48[0][0]       
                                                                 tf_op_layer_Mul_73[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_24 (TensorFlowO [(None,)]            0           tf_op_layer_Square_99[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Mul_74 (TensorFlowO [()]                 0           tf_op_layer_Sub_123[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_49 (TensorFlo [(None,)]            0           tf_op_layer_Sum_24[0][0]         
                                                                 tf_op_layer_Mul_74[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_174 (TensorFlo [()]                 0           tf_op_layer_AddV2_49[0][0]       
__________________________________________________________________________________________________
add_loss_24 (AddLoss)           ()                   0           tf_op_layer_Mean_174[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.0783WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0019s vs `on_train_batch_end` time: 0.1609s). Check your callbacks.
24/24 [==============================] - 1s 22ms/step - loss: 4.0908 - val_loss: 1.9251
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4467 - val_loss: 1.3523
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2154 - val_loss: 1.2718
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1372 - val_loss: 1.1869
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9811 - val_loss: 1.0249
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9020 - val_loss: 0.9687
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8875 - val_loss: 0.9445
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8540 - val_loss: 0.9016
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8339 - val_loss: 0.8822
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8208 - val_loss: 0.8911
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8292 - val_loss: 0.8659
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8129 - val_loss: 0.8850
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8133 - val_loss: 0.8619
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7903 - val_loss: 0.8459
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7967 - val_loss: 0.8665
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7908 - val_loss: 0.8492
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7918 - val_loss: 0.8288
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7904 - val_loss: 0.8363
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7726 - val_loss: 0.8369
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7770 - val_loss: 0.8429
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7613 - val_loss: 0.8044
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7578 - val_loss: 0.8297
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7557 - val_loss: 0.8118
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7505 - val_loss: 0.8027
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7474 - val_loss: 0.8094
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7418 - val_loss: 0.8342
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7397 - val_loss: 0.8014
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7425 - val_loss: 0.8245
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7368 - val_loss: 0.7897
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7312 - val_loss: 0.8042
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7318 - val_loss: 0.8076
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7369 - val_loss: 0.7785
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7347 - val_loss: 0.7860
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7439 - val_loss: 0.7905
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7297 - val_loss: 0.8072
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7315 - val_loss: 0.7759
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7238 - val_loss: 0.7848
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7273 - val_loss: 0.7774
Epoch 39/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7299 - val_loss: 0.7770
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7309 - val_loss: 0.7621
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7309 - val_loss: 0.7763
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7183 - val_loss: 0.7894
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7178 - val_loss: 0.7672
Epoch 44/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7185 - val_loss: 0.7925
Epoch 45/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7226 - val_loss: 0.7656
Epoch 46/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7160 - val_loss: 0.7651
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7226 - val_loss: 0.7677
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7181 - val_loss: 0.7551
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7151 - val_loss: 0.7472
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7032 - val_loss: 0.7578
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7088 - val_loss: 0.7472
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7147 - val_loss: 0.7891
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7050 - val_loss: 0.7593
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7016 - val_loss: 0.7449
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6989 - val_loss: 0.7683
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7041 - val_loss: 0.7486
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7001 - val_loss: 0.7477
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7045 - val_loss: 0.7417
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7051 - val_loss: 0.7939
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6975 - val_loss: 0.7494
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7000 - val_loss: 0.7573
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7003 - val_loss: 0.7861
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6849 - val_loss: 0.7442
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6956 - val_loss: 0.7824
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6935 - val_loss: 0.7541
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6856 - val_loss: 0.7387
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6981 - val_loss: 0.7518
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6949 - val_loss: 0.7341
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6928 - val_loss: 0.7588
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6864 - val_loss: 0.7483
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6932 - val_loss: 0.7310
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7095 - val_loss: 0.7484
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6854 - val_loss: 0.7395
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6894 - val_loss: 0.7465
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6939 - val_loss: 0.7611
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6806 - val_loss: 0.7257
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6891 - val_loss: 0.7396
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6838 - val_loss: 0.7877
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6892 - val_loss: 0.7426
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6907 - val_loss: 0.7353
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6832 - val_loss: 0.7334
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6905 - val_loss: 0.7404
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6871 - val_loss: 0.7459
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6794 - val_loss: 0.7566
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6925 - val_loss: 0.7272
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6818 - val_loss: 0.7211
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6766 - val_loss: 0.7335
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6759 - val_loss: 0.7208
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6838 - val_loss: 0.7582
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6894 - val_loss: 0.7221
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6717 - val_loss: 0.7284
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6780 - val_loss: 0.7210
Epoch 93/100
24/24 [==============================] - 0s 5ms/step - loss: 0.6697 - val_loss: 0.7256
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6860 - val_loss: 0.7356
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6772 - val_loss: 0.7166
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6757 - val_loss: 0.7162
Epoch 97/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6841 - val_loss: 0.7186
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6794 - val_loss: 0.7325
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6744 - val_loss: 0.7189
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6697 - val_loss: 0.7188
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_77 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_250 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_250 ( [()]                 0           tf_op_layer_Shape_250[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_25 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_250[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_25[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_75 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_25 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_75[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_251 (TensorFl [(2,)]               0           tf_op_layer_Add_25[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_253 (TensorFl [(2,)]               0           tf_op_layer_Add_25[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_252 (TensorFl [(2,)]               0           tf_op_layer_Add_25[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_254 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_256 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_255 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_257 (TensorFl [(2,)]               0           tf_op_layer_Add_25[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_259 (TensorFl [(2,)]               0           tf_op_layer_Add_25[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_258 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_251 ( [()]                 0           tf_op_layer_Shape_251[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_253 ( [()]                 0           tf_op_layer_Shape_253[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_252 ( [()]                 0           tf_op_layer_Shape_252[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_254 ( [()]                 0           tf_op_layer_Shape_254[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_256 ( [()]                 0           tf_op_layer_Shape_256[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_255 ( [()]                 0           tf_op_layer_Shape_255[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_257 ( [()]                 0           tf_op_layer_Shape_257[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_259 ( [()]                 0           tf_op_layer_Shape_259[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_258 ( [()]                 0           tf_op_layer_Shape_258[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_150/shape ( [(3,)]               0           tf_op_layer_strided_slice_251[0][
                                                                 tf_op_layer_strided_slice_253[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_151/shape ( [(3,)]               0           tf_op_layer_strided_slice_252[0][
                                                                 tf_op_layer_strided_slice_253[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_152/shape ( [(3,)]               0           tf_op_layer_strided_slice_254[0][
                                                                 tf_op_layer_strided_slice_256[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_153/shape ( [(3,)]               0           tf_op_layer_strided_slice_255[0][
                                                                 tf_op_layer_strided_slice_256[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_154/shape ( [(3,)]               0           tf_op_layer_strided_slice_257[0][
                                                                 tf_op_layer_strided_slice_259[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_155/shape ( [(3,)]               0           tf_op_layer_strided_slice_258[0][
                                                                 tf_op_layer_strided_slice_259[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_150 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_25[0][0]         
                                                                 tf_op_layer_Reshape_150/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_150/multiples  [(3,)]               0           tf_op_layer_strided_slice_252[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_151 (Tensor [(1, None, None)]    0           tf_op_layer_Add_25[0][0]         
                                                                 tf_op_layer_Reshape_151/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_151/multiples  [(3,)]               0           tf_op_layer_strided_slice_251[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_152 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_152/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_152/multiples  [(3,)]               0           tf_op_layer_strided_slice_255[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_153 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_153/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_153/multiples  [(3,)]               0           tf_op_layer_strided_slice_254[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_154 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_25[0][0]         
                                                                 tf_op_layer_Reshape_154/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_154/multiples  [(3,)]               0           tf_op_layer_strided_slice_258[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_155 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_155/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_155/multiples  [(3,)]               0           tf_op_layer_strided_slice_257[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_150 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_150[0][0]    
                                                                 tf_op_layer_Tile_150/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_151 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_151[0][0]    
                                                                 tf_op_layer_Tile_151/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_152 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_152[0][0]    
                                                                 tf_op_layer_Tile_152/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_153 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_153[0][0]    
                                                                 tf_op_layer_Tile_153/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_154 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_154[0][0]    
                                                                 tf_op_layer_Tile_154/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_155 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_155[0][0]    
                                                                 tf_op_layer_Tile_155/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_125 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_150[0][0]       
                                                                 tf_op_layer_Tile_151[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_126 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_152[0][0]       
                                                                 tf_op_layer_Tile_153[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_127 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_154[0][0]       
                                                                 tf_op_layer_Tile_155[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_100 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_125[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_101 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_126[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_102 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_127[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_175 (TensorFlo [(None, None)]       0           tf_op_layer_Square_100[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_176 (TensorFlo [(None, None)]       0           tf_op_layer_Square_101[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_177 (TensorFlo [(None, None)]       0           tf_op_layer_Square_102[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_75 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_175[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_75 (TensorFlow [()]                 0           tf_op_layer_strided_slice_253[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_76 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_176[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_76 (TensorFlow [()]                 0           tf_op_layer_strided_slice_256[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_77 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_177[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_77 (TensorFlow [()]                 0           tf_op_layer_strided_slice_259[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_75 (TensorF [(None, None)]       0           tf_op_layer_Neg_75[0][0]         
                                                                 tf_op_layer_Cast_75[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_76 (TensorF [(None, None)]       0           tf_op_layer_Neg_76[0][0]         
                                                                 tf_op_layer_Cast_76[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_77 (TensorF [(None, None)]       0           tf_op_layer_Neg_77[0][0]         
                                                                 tf_op_layer_Cast_77[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_75 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_75[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_76 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_76[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_77 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_77[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_178 (TensorFlo [()]                 0           tf_op_layer_Exp_75[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_179 (TensorFlo [()]                 0           tf_op_layer_Exp_76[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_180 (TensorFlo [()]                 0           tf_op_layer_Exp_77[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_129 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_50 (TensorFlo [()]                 0           tf_op_layer_Mean_178[0][0]       
                                                                 tf_op_layer_Mean_179[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_76 (TensorFlowO [()]                 0           tf_op_layer_Mean_180[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_103 (TensorF [(None, 97)]         0           tf_op_layer_Sub_129[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_128 (TensorFlow [()]                 0           tf_op_layer_AddV2_50[0][0]       
                                                                 tf_op_layer_Mul_76[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_25 (TensorFlowO [(None,)]            0           tf_op_layer_Square_103[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_77 (TensorFlowO [()]                 0           tf_op_layer_Sub_128[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_51 (TensorFlo [(None,)]            0           tf_op_layer_Sum_25[0][0]         
                                                                 tf_op_layer_Mul_77[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_181 (TensorFlo [()]                 0           tf_op_layer_AddV2_51[0][0]       
__________________________________________________________________________________________________
add_loss_25 (AddLoss)           ()                   0           tf_op_layer_Mean_181[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 11.0093WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.1196s). Check your callbacks.
24/24 [==============================] - 0s 16ms/step - loss: 7.9753 - val_loss: 5.4462
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.7754 - val_loss: 3.8143
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 3.1129 - val_loss: 2.6792
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.1988 - val_loss: 2.3119
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8923 - val_loss: 2.1823
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7788 - val_loss: 2.0690
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7005 - val_loss: 2.0154
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6284 - val_loss: 1.9747
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5854 - val_loss: 1.8949
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5630 - val_loss: 1.8530
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5013 - val_loss: 1.7802
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4778 - val_loss: 1.8059
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4654 - val_loss: 1.7086
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4089 - val_loss: 1.6578
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3914 - val_loss: 1.6485
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3715 - val_loss: 1.6120
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3106 - val_loss: 1.5596
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2774 - val_loss: 1.5416
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2598 - val_loss: 1.5151
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2205 - val_loss: 1.4429
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1870 - val_loss: 1.4962
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1796 - val_loss: 1.4363
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1442 - val_loss: 1.3919
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1376 - val_loss: 1.4337
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1259 - val_loss: 1.3567
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1203 - val_loss: 1.3292
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1181 - val_loss: 1.3688
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1096 - val_loss: 1.3226
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1010 - val_loss: 1.3259
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0726 - val_loss: 1.3326
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0796 - val_loss: 1.3248
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0964 - val_loss: 1.3146
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0708 - val_loss: 1.3198
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0854 - val_loss: 1.2698
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0798 - val_loss: 1.2688
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0598 - val_loss: 1.2846
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0523 - val_loss: 1.3014
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0704 - val_loss: 1.3240
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0528 - val_loss: 1.3104
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0370 - val_loss: 1.3475
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0269 - val_loss: 1.2741
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0318 - val_loss: 1.2938
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0443 - val_loss: 1.2592
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0373 - val_loss: 1.3144
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0327 - val_loss: 1.2722
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0246 - val_loss: 1.2813
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0309 - val_loss: 1.2935
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0231 - val_loss: 1.2674
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0279 - val_loss: 1.2434
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0223 - val_loss: 1.2223
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0113 - val_loss: 1.2299
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0343 - val_loss: 1.2342
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0276 - val_loss: 1.2337
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0100 - val_loss: 1.2570
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0081 - val_loss: 1.2099
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0096 - val_loss: 1.2373
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0116 - val_loss: 1.2346
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0062 - val_loss: 1.2058
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0090 - val_loss: 1.2571
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0137 - val_loss: 1.2271
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0057 - val_loss: 1.2327
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0047 - val_loss: 1.2467
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9913 - val_loss: 1.2173
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9889 - val_loss: 1.1968
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9976 - val_loss: 1.2246
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0026 - val_loss: 1.1973
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0006 - val_loss: 1.2197
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9943 - val_loss: 1.2222
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9987 - val_loss: 1.2315
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9989 - val_loss: 1.2364
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9927 - val_loss: 1.2387
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0011 - val_loss: 1.2203
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0209 - val_loss: 1.2602
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9904 - val_loss: 1.1996
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9908 - val_loss: 1.2214
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9826 - val_loss: 1.2121
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9802 - val_loss: 1.1966
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9854 - val_loss: 1.2016
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9985 - val_loss: 1.2134
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9763 - val_loss: 1.1932
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9837 - val_loss: 1.2003
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9833 - val_loss: 1.2112
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9699 - val_loss: 1.2193
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9805 - val_loss: 1.2379
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9929 - val_loss: 1.2135
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9712 - val_loss: 1.1722
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9752 - val_loss: 1.1961
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9740 - val_loss: 1.1808
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9870 - val_loss: 1.1760
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9694 - val_loss: 1.2127
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9839 - val_loss: 1.1964
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9730 - val_loss: 1.2121
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9800 - val_loss: 1.1713
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9666 - val_loss: 1.1747
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9732 - val_loss: 1.1935
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9683 - val_loss: 1.1901
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9680 - val_loss: 1.1566
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9635 - val_loss: 1.2268
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9667 - val_loss: 1.1627
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9683 - val_loss: 1.2012
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.31 seconds.
    Calculating affinities...
    Calculated affinities in 0.02 seconds.
  Calculated graph and diffusion operator in 0.34 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.80 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.26 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 9.67 seconds.
Calculated PHATE in 12.09 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_78 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_79 (Dense)                (None, 32)           2080        dense_78[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_79[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_79[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_80 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_81 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_82 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_78 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_79 (Dense)                (None, 32)           2080        dense_78[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_79[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_79[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_260 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_260 ( [()]                 0           tf_op_layer_Shape_260[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_26 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_260[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_26[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_78 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_26 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_78[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_261 (TensorFl [(2,)]               0           tf_op_layer_Add_26[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_263 (TensorFl [(2,)]               0           tf_op_layer_Add_26[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_262 (TensorFl [(2,)]               0           tf_op_layer_Add_26[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_264 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_266 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_265 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_267 (TensorFl [(2,)]               0           tf_op_layer_Add_26[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_269 (TensorFl [(2,)]               0           tf_op_layer_Add_26[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_268 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_261 ( [()]                 0           tf_op_layer_Shape_261[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_263 ( [()]                 0           tf_op_layer_Shape_263[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_262 ( [()]                 0           tf_op_layer_Shape_262[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_264 ( [()]                 0           tf_op_layer_Shape_264[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_266 ( [()]                 0           tf_op_layer_Shape_266[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_265 ( [()]                 0           tf_op_layer_Shape_265[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_267 ( [()]                 0           tf_op_layer_Shape_267[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_269 ( [()]                 0           tf_op_layer_Shape_269[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_268 ( [()]                 0           tf_op_layer_Shape_268[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_156/shape ( [(3,)]               0           tf_op_layer_strided_slice_261[0][
                                                                 tf_op_layer_strided_slice_263[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_157/shape ( [(3,)]               0           tf_op_layer_strided_slice_262[0][
                                                                 tf_op_layer_strided_slice_263[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_158/shape ( [(3,)]               0           tf_op_layer_strided_slice_264[0][
                                                                 tf_op_layer_strided_slice_266[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_159/shape ( [(3,)]               0           tf_op_layer_strided_slice_265[0][
                                                                 tf_op_layer_strided_slice_266[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_160/shape ( [(3,)]               0           tf_op_layer_strided_slice_267[0][
                                                                 tf_op_layer_strided_slice_269[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_161/shape ( [(3,)]               0           tf_op_layer_strided_slice_268[0][
                                                                 tf_op_layer_strided_slice_269[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_156 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_26[0][0]         
                                                                 tf_op_layer_Reshape_156/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_156/multiples  [(3,)]               0           tf_op_layer_strided_slice_262[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_157 (Tensor [(1, None, None)]    0           tf_op_layer_Add_26[0][0]         
                                                                 tf_op_layer_Reshape_157/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_157/multiples  [(3,)]               0           tf_op_layer_strided_slice_261[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_158 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_158/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_158/multiples  [(3,)]               0           tf_op_layer_strided_slice_265[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_159 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_159/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_159/multiples  [(3,)]               0           tf_op_layer_strided_slice_264[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_160 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_26[0][0]         
                                                                 tf_op_layer_Reshape_160/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_160/multiples  [(3,)]               0           tf_op_layer_strided_slice_268[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_161 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_161/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_161/multiples  [(3,)]               0           tf_op_layer_strided_slice_267[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_156 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_156[0][0]    
                                                                 tf_op_layer_Tile_156/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_157 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_157[0][0]    
                                                                 tf_op_layer_Tile_157/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_158 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_158[0][0]    
                                                                 tf_op_layer_Tile_158/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_159 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_159[0][0]    
                                                                 tf_op_layer_Tile_159/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_160 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_160[0][0]    
                                                                 tf_op_layer_Tile_160/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_161 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_161[0][0]    
                                                                 tf_op_layer_Tile_161/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_130 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_156[0][0]       
                                                                 tf_op_layer_Tile_157[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_131 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_158[0][0]       
                                                                 tf_op_layer_Tile_159[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_132 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_160[0][0]       
                                                                 tf_op_layer_Tile_161[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_104 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_130[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_105 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_131[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_106 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_132[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_182 (TensorFlo [(None, None)]       0           tf_op_layer_Square_104[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_183 (TensorFlo [(None, None)]       0           tf_op_layer_Square_105[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_184 (TensorFlo [(None, None)]       0           tf_op_layer_Square_106[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_78 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_182[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_78 (TensorFlow [()]                 0           tf_op_layer_strided_slice_263[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_79 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_183[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_79 (TensorFlow [()]                 0           tf_op_layer_strided_slice_266[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_80 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_184[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_80 (TensorFlow [()]                 0           tf_op_layer_strided_slice_269[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_78 (TensorF [(None, None)]       0           tf_op_layer_Neg_78[0][0]         
                                                                 tf_op_layer_Cast_78[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_79 (TensorF [(None, None)]       0           tf_op_layer_Neg_79[0][0]         
                                                                 tf_op_layer_Cast_79[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_80 (TensorF [(None, None)]       0           tf_op_layer_Neg_80[0][0]         
                                                                 tf_op_layer_Cast_80[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_78 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_78[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_79 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_79[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_80 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_80[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_185 (TensorFlo [()]                 0           tf_op_layer_Exp_78[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_186 (TensorFlo [()]                 0           tf_op_layer_Exp_79[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_187 (TensorFlo [()]                 0           tf_op_layer_Exp_80[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_134 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_52 (TensorFlo [()]                 0           tf_op_layer_Mean_185[0][0]       
                                                                 tf_op_layer_Mean_186[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_79 (TensorFlowO [()]                 0           tf_op_layer_Mean_187[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_107 (TensorF [(None, 97)]         0           tf_op_layer_Sub_134[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_133 (TensorFlow [()]                 0           tf_op_layer_AddV2_52[0][0]       
                                                                 tf_op_layer_Mul_79[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_26 (TensorFlowO [(None,)]            0           tf_op_layer_Square_107[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_80 (TensorFlowO [()]                 0           tf_op_layer_Sub_133[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_53 (TensorFlo [(None,)]            0           tf_op_layer_Sum_26[0][0]         
                                                                 tf_op_layer_Mul_80[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_188 (TensorFlo [()]                 0           tf_op_layer_AddV2_53[0][0]       
__________________________________________________________________________________________________
add_loss_26 (AddLoss)           ()                   0           tf_op_layer_Mean_188[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.5687WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0019s vs `on_train_batch_end` time: 0.1910s). Check your callbacks.
24/24 [==============================] - 1s 30ms/step - loss: 4.6354 - val_loss: 1.8314
Epoch 2/100
24/24 [==============================] - 0s 7ms/step - loss: 1.5651 - val_loss: 1.3017
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3234 - val_loss: 1.1279
Epoch 4/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1790 - val_loss: 1.0436
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0476 - val_loss: 0.9315
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9179 - val_loss: 0.8997
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8894 - val_loss: 0.8722
Epoch 8/100
24/24 [==============================] - 0s 5ms/step - loss: 0.8516 - val_loss: 0.8637
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8499 - val_loss: 0.8686
Epoch 10/100
24/24 [==============================] - 0s 5ms/step - loss: 0.8362 - val_loss: 0.8395
Epoch 11/100
24/24 [==============================] - 0s 6ms/step - loss: 0.8363 - val_loss: 0.8532
Epoch 12/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8307 - val_loss: 0.8406
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8266 - val_loss: 0.8251
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8142 - val_loss: 0.8248
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8169 - val_loss: 0.8059
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8056 - val_loss: 0.8157
Epoch 17/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8077 - val_loss: 0.8188
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8164 - val_loss: 0.8155
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7998 - val_loss: 0.8324
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7907 - val_loss: 0.8278
Epoch 21/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7841 - val_loss: 0.8089
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7882 - val_loss: 0.7853
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7731 - val_loss: 0.7866
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7710 - val_loss: 0.8239
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7775 - val_loss: 0.8022
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7611 - val_loss: 0.8108
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7760 - val_loss: 0.7919
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7733 - val_loss: 0.8012
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7726 - val_loss: 0.8013
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7709 - val_loss: 0.7795
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7622 - val_loss: 0.7977
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7581 - val_loss: 0.7796
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7643 - val_loss: 0.7897
Epoch 34/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7567 - val_loss: 0.7920
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7490 - val_loss: 0.7878
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7608 - val_loss: 0.8022
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7461 - val_loss: 0.7793
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7394 - val_loss: 0.7741
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7457 - val_loss: 0.7788
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7448 - val_loss: 0.8398
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7473 - val_loss: 0.7724
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7393 - val_loss: 0.7883
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7419 - val_loss: 0.7706
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7293 - val_loss: 0.7734
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7379 - val_loss: 0.7743
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7346 - val_loss: 0.7853
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7340 - val_loss: 0.7676
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7324 - val_loss: 0.7789
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7266 - val_loss: 0.7869
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7274 - val_loss: 0.7730
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7244 - val_loss: 0.7873
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7286 - val_loss: 0.7696
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7279 - val_loss: 0.7607
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7269 - val_loss: 0.7621
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7250 - val_loss: 0.7853
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7329 - val_loss: 0.7640
Epoch 57/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7294 - val_loss: 0.7731
Epoch 58/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7300 - val_loss: 0.7628
Epoch 59/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7215 - val_loss: 0.7640
Epoch 60/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7359 - val_loss: 0.7680
Epoch 61/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7292 - val_loss: 0.7586
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7297 - val_loss: 0.7726
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7248 - val_loss: 0.7623
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7174 - val_loss: 0.7762
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7061 - val_loss: 0.7728
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7244 - val_loss: 0.7664
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7172 - val_loss: 0.7731
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7107 - val_loss: 0.7628
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7139 - val_loss: 0.7668
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7190 - val_loss: 0.7647
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7036 - val_loss: 0.7511
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7118 - val_loss: 0.7803
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7076 - val_loss: 0.7695
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7030 - val_loss: 0.7740
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7202 - val_loss: 0.7631
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7096 - val_loss: 0.7593
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7149 - val_loss: 0.7548
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7089 - val_loss: 0.7717
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7055 - val_loss: 0.7658
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7061 - val_loss: 0.7478
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7129 - val_loss: 0.7731
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7053 - val_loss: 0.7604
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7008 - val_loss: 0.7635
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6990 - val_loss: 0.7698
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6954 - val_loss: 0.7564
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6989 - val_loss: 0.7694
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6994 - val_loss: 0.7448
Epoch 88/100
24/24 [==============================] - ETA: 0s - loss: 0.698 - 0s 3ms/step - loss: 0.6988 - val_loss: 0.7625
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6985 - val_loss: 0.7479
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6963 - val_loss: 0.7451
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7050 - val_loss: 0.7342
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7061 - val_loss: 0.7411
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6961 - val_loss: 0.7513
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7046 - val_loss: 0.7514
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6908 - val_loss: 0.7726
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6921 - val_loss: 0.7657
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6960 - val_loss: 0.7610
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6913 - val_loss: 0.7421
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7012 - val_loss: 0.7437
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7018 - val_loss: 0.7428
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_83 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_270 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_270 ( [()]                 0           tf_op_layer_Shape_270[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_27 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_270[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_27[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_81 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_27 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_81[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_271 (TensorFl [(2,)]               0           tf_op_layer_Add_27[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_273 (TensorFl [(2,)]               0           tf_op_layer_Add_27[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_272 (TensorFl [(2,)]               0           tf_op_layer_Add_27[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_274 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_276 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_275 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_277 (TensorFl [(2,)]               0           tf_op_layer_Add_27[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_279 (TensorFl [(2,)]               0           tf_op_layer_Add_27[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_278 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_271 ( [()]                 0           tf_op_layer_Shape_271[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_273 ( [()]                 0           tf_op_layer_Shape_273[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_272 ( [()]                 0           tf_op_layer_Shape_272[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_274 ( [()]                 0           tf_op_layer_Shape_274[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_276 ( [()]                 0           tf_op_layer_Shape_276[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_275 ( [()]                 0           tf_op_layer_Shape_275[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_277 ( [()]                 0           tf_op_layer_Shape_277[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_279 ( [()]                 0           tf_op_layer_Shape_279[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_278 ( [()]                 0           tf_op_layer_Shape_278[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_162/shape ( [(3,)]               0           tf_op_layer_strided_slice_271[0][
                                                                 tf_op_layer_strided_slice_273[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_163/shape ( [(3,)]               0           tf_op_layer_strided_slice_272[0][
                                                                 tf_op_layer_strided_slice_273[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_164/shape ( [(3,)]               0           tf_op_layer_strided_slice_274[0][
                                                                 tf_op_layer_strided_slice_276[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_165/shape ( [(3,)]               0           tf_op_layer_strided_slice_275[0][
                                                                 tf_op_layer_strided_slice_276[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_166/shape ( [(3,)]               0           tf_op_layer_strided_slice_277[0][
                                                                 tf_op_layer_strided_slice_279[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_167/shape ( [(3,)]               0           tf_op_layer_strided_slice_278[0][
                                                                 tf_op_layer_strided_slice_279[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_162 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_27[0][0]         
                                                                 tf_op_layer_Reshape_162/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_162/multiples  [(3,)]               0           tf_op_layer_strided_slice_272[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_163 (Tensor [(1, None, None)]    0           tf_op_layer_Add_27[0][0]         
                                                                 tf_op_layer_Reshape_163/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_163/multiples  [(3,)]               0           tf_op_layer_strided_slice_271[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_164 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_164/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_164/multiples  [(3,)]               0           tf_op_layer_strided_slice_275[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_165 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_165/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_165/multiples  [(3,)]               0           tf_op_layer_strided_slice_274[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_166 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_27[0][0]         
                                                                 tf_op_layer_Reshape_166/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_166/multiples  [(3,)]               0           tf_op_layer_strided_slice_278[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_167 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_167/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_167/multiples  [(3,)]               0           tf_op_layer_strided_slice_277[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_162 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_162[0][0]    
                                                                 tf_op_layer_Tile_162/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_163 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_163[0][0]    
                                                                 tf_op_layer_Tile_163/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_164 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_164[0][0]    
                                                                 tf_op_layer_Tile_164/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_165 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_165[0][0]    
                                                                 tf_op_layer_Tile_165/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_166 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_166[0][0]    
                                                                 tf_op_layer_Tile_166/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_167 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_167[0][0]    
                                                                 tf_op_layer_Tile_167/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_135 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_162[0][0]       
                                                                 tf_op_layer_Tile_163[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_136 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_164[0][0]       
                                                                 tf_op_layer_Tile_165[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_137 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_166[0][0]       
                                                                 tf_op_layer_Tile_167[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_108 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_135[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_109 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_136[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_110 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_137[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_189 (TensorFlo [(None, None)]       0           tf_op_layer_Square_108[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_190 (TensorFlo [(None, None)]       0           tf_op_layer_Square_109[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_191 (TensorFlo [(None, None)]       0           tf_op_layer_Square_110[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_81 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_189[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_81 (TensorFlow [()]                 0           tf_op_layer_strided_slice_273[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_82 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_190[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_82 (TensorFlow [()]                 0           tf_op_layer_strided_slice_276[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_83 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_191[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_83 (TensorFlow [()]                 0           tf_op_layer_strided_slice_279[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_81 (TensorF [(None, None)]       0           tf_op_layer_Neg_81[0][0]         
                                                                 tf_op_layer_Cast_81[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_82 (TensorF [(None, None)]       0           tf_op_layer_Neg_82[0][0]         
                                                                 tf_op_layer_Cast_82[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_83 (TensorF [(None, None)]       0           tf_op_layer_Neg_83[0][0]         
                                                                 tf_op_layer_Cast_83[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_81 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_81[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_82 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_82[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_83 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_83[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_192 (TensorFlo [()]                 0           tf_op_layer_Exp_81[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_193 (TensorFlo [()]                 0           tf_op_layer_Exp_82[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_194 (TensorFlo [()]                 0           tf_op_layer_Exp_83[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_139 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_54 (TensorFlo [()]                 0           tf_op_layer_Mean_192[0][0]       
                                                                 tf_op_layer_Mean_193[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_82 (TensorFlowO [()]                 0           tf_op_layer_Mean_194[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_111 (TensorF [(None, 97)]         0           tf_op_layer_Sub_139[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_138 (TensorFlow [()]                 0           tf_op_layer_AddV2_54[0][0]       
                                                                 tf_op_layer_Mul_82[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_27 (TensorFlowO [(None,)]            0           tf_op_layer_Square_111[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_83 (TensorFlowO [()]                 0           tf_op_layer_Sub_138[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_55 (TensorFlo [(None,)]            0           tf_op_layer_Sum_27[0][0]         
                                                                 tf_op_layer_Mul_83[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_195 (TensorFlo [()]                 0           tf_op_layer_AddV2_55[0][0]       
__________________________________________________________________________________________________
add_loss_27 (AddLoss)           ()                   0           tf_op_layer_Mean_195[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.8359 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1185s). Check your callbacks.
24/24 [==============================] - 0s 16ms/step - loss: 8.1469 - val_loss: 6.2673
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.7194 - val_loss: 3.7355
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.9008 - val_loss: 2.3561
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.1016 - val_loss: 1.8489
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8541 - val_loss: 1.7386
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7387 - val_loss: 1.6130
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6365 - val_loss: 1.5346
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6019 - val_loss: 1.5006
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5237 - val_loss: 1.4287
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4925 - val_loss: 1.4142
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4657 - val_loss: 1.3657
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4277 - val_loss: 1.3422
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4146 - val_loss: 1.3094
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4059 - val_loss: 1.2860
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3446 - val_loss: 1.3126
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3252 - val_loss: 1.2557
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3143 - val_loss: 1.2260
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2826 - val_loss: 1.1975
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2676 - val_loss: 1.1751
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2360 - val_loss: 1.2187
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2301 - val_loss: 1.1669
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2412 - val_loss: 1.1554
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2328 - val_loss: 1.1234
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1985 - val_loss: 1.1390
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1880 - val_loss: 1.1375
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1807 - val_loss: 1.0837
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1789 - val_loss: 1.1042
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1649 - val_loss: 1.0800
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1744 - val_loss: 1.1058
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1461 - val_loss: 1.0644
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1398 - val_loss: 1.0759
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1317 - val_loss: 1.0884
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1313 - val_loss: 1.0784
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1327 - val_loss: 1.0510
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1202 - val_loss: 1.0442
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1216 - val_loss: 1.0348
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1118 - val_loss: 1.0288
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1164 - val_loss: 1.0246
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1058 - val_loss: 1.0381
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1014 - val_loss: 1.0028
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0946 - val_loss: 1.0001
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1082 - val_loss: 1.0116
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0893 - val_loss: 1.0029
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0910 - val_loss: 1.0429
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0983 - val_loss: 0.9689
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0665 - val_loss: 0.9672
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0878 - val_loss: 1.0041
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0830 - val_loss: 1.0004
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0714 - val_loss: 0.9942
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0670 - val_loss: 0.9816
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0832 - val_loss: 0.9550
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0671 - val_loss: 0.9791
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0525 - val_loss: 0.9789
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0530 - val_loss: 0.9844
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0690 - val_loss: 0.9873
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0599 - val_loss: 0.9823
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0422 - val_loss: 0.9570
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0524 - val_loss: 0.9512
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0390 - val_loss: 0.9589
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0408 - val_loss: 0.9678
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0497 - val_loss: 0.9575
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0434 - val_loss: 0.9669
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0268 - val_loss: 0.9292
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0257 - val_loss: 0.9265
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0412 - val_loss: 0.9579
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0260 - val_loss: 0.9663
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0353 - val_loss: 0.9315
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0262 - val_loss: 0.9530
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0225 - val_loss: 0.9266
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0261 - val_loss: 0.9384
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0224 - val_loss: 0.9298
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0258 - val_loss: 0.9211
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0244 - val_loss: 0.9468
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0410 - val_loss: 0.9379
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0173 - val_loss: 0.9363
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0201 - val_loss: 0.9584
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0196 - val_loss: 0.9194
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0192 - val_loss: 0.9219
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0192 - val_loss: 0.9241
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0149 - val_loss: 0.9414
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0188 - val_loss: 0.9079
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0203 - val_loss: 0.9211
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0071 - val_loss: 0.9212
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0167 - val_loss: 0.9155
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0175 - val_loss: 0.9183
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0172 - val_loss: 0.9285
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0024 - val_loss: 0.9265
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9992 - val_loss: 0.9503
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9929 - val_loss: 0.9045
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9928 - val_loss: 0.9216
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0078 - val_loss: 0.9122
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0072 - val_loss: 0.9151
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0033 - val_loss: 0.9337
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0289 - val_loss: 0.9047
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9956 - val_loss: 0.8892
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9981 - val_loss: 0.9126
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0066 - val_loss: 0.9352
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0146 - val_loss: 0.9253
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0041 - val_loss: 0.8970
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0055 - val_loss: 0.9088
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.30 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.33 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.81 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.24 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 8.27 seconds.
Calculated PHATE in 10.67 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_84 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_85 (Dense)                (None, 32)           2080        dense_84[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_85[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_85[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_86 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_87 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_88 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_84 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_85 (Dense)                (None, 32)           2080        dense_84[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_85[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_85[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_280 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_280 ( [()]                 0           tf_op_layer_Shape_280[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_28 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_280[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_28[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_84 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_28 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_84[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_281 (TensorFl [(2,)]               0           tf_op_layer_Add_28[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_283 (TensorFl [(2,)]               0           tf_op_layer_Add_28[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_282 (TensorFl [(2,)]               0           tf_op_layer_Add_28[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_284 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_286 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_285 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_287 (TensorFl [(2,)]               0           tf_op_layer_Add_28[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_289 (TensorFl [(2,)]               0           tf_op_layer_Add_28[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_288 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_281 ( [()]                 0           tf_op_layer_Shape_281[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_283 ( [()]                 0           tf_op_layer_Shape_283[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_282 ( [()]                 0           tf_op_layer_Shape_282[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_284 ( [()]                 0           tf_op_layer_Shape_284[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_286 ( [()]                 0           tf_op_layer_Shape_286[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_285 ( [()]                 0           tf_op_layer_Shape_285[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_287 ( [()]                 0           tf_op_layer_Shape_287[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_289 ( [()]                 0           tf_op_layer_Shape_289[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_288 ( [()]                 0           tf_op_layer_Shape_288[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_168/shape ( [(3,)]               0           tf_op_layer_strided_slice_281[0][
                                                                 tf_op_layer_strided_slice_283[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_169/shape ( [(3,)]               0           tf_op_layer_strided_slice_282[0][
                                                                 tf_op_layer_strided_slice_283[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_170/shape ( [(3,)]               0           tf_op_layer_strided_slice_284[0][
                                                                 tf_op_layer_strided_slice_286[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_171/shape ( [(3,)]               0           tf_op_layer_strided_slice_285[0][
                                                                 tf_op_layer_strided_slice_286[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_172/shape ( [(3,)]               0           tf_op_layer_strided_slice_287[0][
                                                                 tf_op_layer_strided_slice_289[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_173/shape ( [(3,)]               0           tf_op_layer_strided_slice_288[0][
                                                                 tf_op_layer_strided_slice_289[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_168 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_28[0][0]         
                                                                 tf_op_layer_Reshape_168/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_168/multiples  [(3,)]               0           tf_op_layer_strided_slice_282[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_169 (Tensor [(1, None, None)]    0           tf_op_layer_Add_28[0][0]         
                                                                 tf_op_layer_Reshape_169/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_169/multiples  [(3,)]               0           tf_op_layer_strided_slice_281[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_170 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_170/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_170/multiples  [(3,)]               0           tf_op_layer_strided_slice_285[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_171 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_171/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_171/multiples  [(3,)]               0           tf_op_layer_strided_slice_284[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_172 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_28[0][0]         
                                                                 tf_op_layer_Reshape_172/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_172/multiples  [(3,)]               0           tf_op_layer_strided_slice_288[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_173 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_173/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_173/multiples  [(3,)]               0           tf_op_layer_strided_slice_287[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_168 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_168[0][0]    
                                                                 tf_op_layer_Tile_168/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_169 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_169[0][0]    
                                                                 tf_op_layer_Tile_169/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_170 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_170[0][0]    
                                                                 tf_op_layer_Tile_170/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_171 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_171[0][0]    
                                                                 tf_op_layer_Tile_171/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_172 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_172[0][0]    
                                                                 tf_op_layer_Tile_172/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_173 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_173[0][0]    
                                                                 tf_op_layer_Tile_173/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_140 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_168[0][0]       
                                                                 tf_op_layer_Tile_169[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_141 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_170[0][0]       
                                                                 tf_op_layer_Tile_171[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_142 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_172[0][0]       
                                                                 tf_op_layer_Tile_173[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_112 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_140[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_113 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_141[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_114 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_142[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_196 (TensorFlo [(None, None)]       0           tf_op_layer_Square_112[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_197 (TensorFlo [(None, None)]       0           tf_op_layer_Square_113[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_198 (TensorFlo [(None, None)]       0           tf_op_layer_Square_114[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_84 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_196[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_84 (TensorFlow [()]                 0           tf_op_layer_strided_slice_283[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_85 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_197[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_85 (TensorFlow [()]                 0           tf_op_layer_strided_slice_286[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_86 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_198[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_86 (TensorFlow [()]                 0           tf_op_layer_strided_slice_289[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_84 (TensorF [(None, None)]       0           tf_op_layer_Neg_84[0][0]         
                                                                 tf_op_layer_Cast_84[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_85 (TensorF [(None, None)]       0           tf_op_layer_Neg_85[0][0]         
                                                                 tf_op_layer_Cast_85[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_86 (TensorF [(None, None)]       0           tf_op_layer_Neg_86[0][0]         
                                                                 tf_op_layer_Cast_86[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_84 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_84[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_85 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_85[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_86 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_86[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_199 (TensorFlo [()]                 0           tf_op_layer_Exp_84[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_200 (TensorFlo [()]                 0           tf_op_layer_Exp_85[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_201 (TensorFlo [()]                 0           tf_op_layer_Exp_86[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_144 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_56 (TensorFlo [()]                 0           tf_op_layer_Mean_199[0][0]       
                                                                 tf_op_layer_Mean_200[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_85 (TensorFlowO [()]                 0           tf_op_layer_Mean_201[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_115 (TensorF [(None, 97)]         0           tf_op_layer_Sub_144[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_143 (TensorFlow [()]                 0           tf_op_layer_AddV2_56[0][0]       
                                                                 tf_op_layer_Mul_85[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_28 (TensorFlowO [(None,)]            0           tf_op_layer_Square_115[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_86 (TensorFlowO [()]                 0           tf_op_layer_Sub_143[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_57 (TensorFlo [(None,)]            0           tf_op_layer_Sum_28[0][0]         
                                                                 tf_op_layer_Mul_86[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_202 (TensorFlo [()]                 0           tf_op_layer_AddV2_57[0][0]       
__________________________________________________________________________________________________
add_loss_28 (AddLoss)           ()                   0           tf_op_layer_Mean_202[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.7232WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.1565s). Check your callbacks.
24/24 [==============================] - 0s 19ms/step - loss: 4.7831 - val_loss: 2.2778
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6527 - val_loss: 1.1913
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2822 - val_loss: 1.1275
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1777 - val_loss: 1.0489
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0976 - val_loss: 0.9943
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0251 - val_loss: 0.9517
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9490 - val_loss: 0.8603
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9009 - val_loss: 0.8212
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8668 - val_loss: 0.8276
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8361 - val_loss: 0.8289
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8317 - val_loss: 0.8159
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8268 - val_loss: 0.8309
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8315 - val_loss: 0.8055
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7998 - val_loss: 0.8116
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8043 - val_loss: 0.7808
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8003 - val_loss: 0.7608
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7935 - val_loss: 0.7644
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7926 - val_loss: 0.7614
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7938 - val_loss: 0.7630
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7913 - val_loss: 0.7643
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7966 - val_loss: 0.7682
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7734 - val_loss: 0.7444
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7707 - val_loss: 0.7447
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7712 - val_loss: 0.7547
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7740 - val_loss: 0.7634
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7699 - val_loss: 0.7398
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7669 - val_loss: 0.7297
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7635 - val_loss: 0.7417
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7684 - val_loss: 0.7623
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7570 - val_loss: 0.7285
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7694 - val_loss: 0.7475
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7513 - val_loss: 0.7455
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7570 - val_loss: 0.7611
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7515 - val_loss: 0.7215
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7535 - val_loss: 0.7531
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7615 - val_loss: 0.7328
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7525 - val_loss: 0.7220
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7492 - val_loss: 0.7297
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7441 - val_loss: 0.7337
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7497 - val_loss: 0.7566
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7432 - val_loss: 0.7054
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7419 - val_loss: 0.7094
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7408 - val_loss: 0.7168
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7492 - val_loss: 0.7195
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7431 - val_loss: 0.7157
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7401 - val_loss: 0.7407
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7338 - val_loss: 0.7130
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7478 - val_loss: 0.7177
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7308 - val_loss: 0.7007
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7367 - val_loss: 0.7179
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7391 - val_loss: 0.7484
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7404 - val_loss: 0.7094
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7418 - val_loss: 0.7430
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7389 - val_loss: 0.7276
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7291 - val_loss: 0.7092
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7345 - val_loss: 0.6952
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7312 - val_loss: 0.7626
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7273 - val_loss: 0.7092
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7414 - val_loss: 0.7138
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7202 - val_loss: 0.6956
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7333 - val_loss: 0.7149
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7284 - val_loss: 0.7052
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7207 - val_loss: 0.7058
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7306 - val_loss: 0.6982
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7246 - val_loss: 0.6989
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7247 - val_loss: 0.7022
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7203 - val_loss: 0.7061
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7155 - val_loss: 0.6981
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7129 - val_loss: 0.6876
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7241 - val_loss: 0.6997
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7326 - val_loss: 0.6964
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7192 - val_loss: 0.6824
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7212 - val_loss: 0.6933
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7138 - val_loss: 0.7230
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7200 - val_loss: 0.6938
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7218 - val_loss: 0.6924
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7175 - val_loss: 0.7004
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7285 - val_loss: 0.7140
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7053 - val_loss: 0.7074
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7119 - val_loss: 0.6993
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7148 - val_loss: 0.7082
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7130 - val_loss: 0.6939
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7154 - val_loss: 0.6858
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7046 - val_loss: 0.7066
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7146 - val_loss: 0.7425
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7061 - val_loss: 0.6961
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7077 - val_loss: 0.7035
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7139 - val_loss: 0.6848
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7033 - val_loss: 0.6952
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7130 - val_loss: 0.6840
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7042 - val_loss: 0.6976
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7100 - val_loss: 0.6788
Epoch 93/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7078 - val_loss: 0.6984
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7155 - val_loss: 0.7031
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7034 - val_loss: 0.6885
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7085 - val_loss: 0.7028
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7037 - val_loss: 0.6695
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7061 - val_loss: 0.6936
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7057 - val_loss: 0.6802
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7006 - val_loss: 0.6998
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_89 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_290 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_290 ( [()]                 0           tf_op_layer_Shape_290[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_29 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_290[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_29[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_87 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_29 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_87[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_291 (TensorFl [(2,)]               0           tf_op_layer_Add_29[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_293 (TensorFl [(2,)]               0           tf_op_layer_Add_29[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_292 (TensorFl [(2,)]               0           tf_op_layer_Add_29[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_294 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_296 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_295 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_297 (TensorFl [(2,)]               0           tf_op_layer_Add_29[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_299 (TensorFl [(2,)]               0           tf_op_layer_Add_29[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_298 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_291 ( [()]                 0           tf_op_layer_Shape_291[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_293 ( [()]                 0           tf_op_layer_Shape_293[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_292 ( [()]                 0           tf_op_layer_Shape_292[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_294 ( [()]                 0           tf_op_layer_Shape_294[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_296 ( [()]                 0           tf_op_layer_Shape_296[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_295 ( [()]                 0           tf_op_layer_Shape_295[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_297 ( [()]                 0           tf_op_layer_Shape_297[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_299 ( [()]                 0           tf_op_layer_Shape_299[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_298 ( [()]                 0           tf_op_layer_Shape_298[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_174/shape ( [(3,)]               0           tf_op_layer_strided_slice_291[0][
                                                                 tf_op_layer_strided_slice_293[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_175/shape ( [(3,)]               0           tf_op_layer_strided_slice_292[0][
                                                                 tf_op_layer_strided_slice_293[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_176/shape ( [(3,)]               0           tf_op_layer_strided_slice_294[0][
                                                                 tf_op_layer_strided_slice_296[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_177/shape ( [(3,)]               0           tf_op_layer_strided_slice_295[0][
                                                                 tf_op_layer_strided_slice_296[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_178/shape ( [(3,)]               0           tf_op_layer_strided_slice_297[0][
                                                                 tf_op_layer_strided_slice_299[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_179/shape ( [(3,)]               0           tf_op_layer_strided_slice_298[0][
                                                                 tf_op_layer_strided_slice_299[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_174 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_29[0][0]         
                                                                 tf_op_layer_Reshape_174/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_174/multiples  [(3,)]               0           tf_op_layer_strided_slice_292[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_175 (Tensor [(1, None, None)]    0           tf_op_layer_Add_29[0][0]         
                                                                 tf_op_layer_Reshape_175/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_175/multiples  [(3,)]               0           tf_op_layer_strided_slice_291[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_176 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_176/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_176/multiples  [(3,)]               0           tf_op_layer_strided_slice_295[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_177 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_177/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_177/multiples  [(3,)]               0           tf_op_layer_strided_slice_294[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_178 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_29[0][0]         
                                                                 tf_op_layer_Reshape_178/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_178/multiples  [(3,)]               0           tf_op_layer_strided_slice_298[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_179 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_179/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_179/multiples  [(3,)]               0           tf_op_layer_strided_slice_297[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_174 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_174[0][0]    
                                                                 tf_op_layer_Tile_174/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_175 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_175[0][0]    
                                                                 tf_op_layer_Tile_175/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_176 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_176[0][0]    
                                                                 tf_op_layer_Tile_176/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_177 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_177[0][0]    
                                                                 tf_op_layer_Tile_177/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_178 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_178[0][0]    
                                                                 tf_op_layer_Tile_178/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_179 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_179[0][0]    
                                                                 tf_op_layer_Tile_179/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_145 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_174[0][0]       
                                                                 tf_op_layer_Tile_175[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_146 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_176[0][0]       
                                                                 tf_op_layer_Tile_177[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_147 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_178[0][0]       
                                                                 tf_op_layer_Tile_179[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_116 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_145[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_117 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_146[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_118 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_147[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_203 (TensorFlo [(None, None)]       0           tf_op_layer_Square_116[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_204 (TensorFlo [(None, None)]       0           tf_op_layer_Square_117[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_205 (TensorFlo [(None, None)]       0           tf_op_layer_Square_118[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_87 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_203[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_87 (TensorFlow [()]                 0           tf_op_layer_strided_slice_293[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_88 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_204[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_88 (TensorFlow [()]                 0           tf_op_layer_strided_slice_296[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_89 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_205[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_89 (TensorFlow [()]                 0           tf_op_layer_strided_slice_299[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_87 (TensorF [(None, None)]       0           tf_op_layer_Neg_87[0][0]         
                                                                 tf_op_layer_Cast_87[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_88 (TensorF [(None, None)]       0           tf_op_layer_Neg_88[0][0]         
                                                                 tf_op_layer_Cast_88[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_89 (TensorF [(None, None)]       0           tf_op_layer_Neg_89[0][0]         
                                                                 tf_op_layer_Cast_89[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_87 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_87[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_88 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_88[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_89 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_89[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_206 (TensorFlo [()]                 0           tf_op_layer_Exp_87[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_207 (TensorFlo [()]                 0           tf_op_layer_Exp_88[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_208 (TensorFlo [()]                 0           tf_op_layer_Exp_89[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_149 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_58 (TensorFlo [()]                 0           tf_op_layer_Mean_206[0][0]       
                                                                 tf_op_layer_Mean_207[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_88 (TensorFlowO [()]                 0           tf_op_layer_Mean_208[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_119 (TensorF [(None, 97)]         0           tf_op_layer_Sub_149[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_148 (TensorFlow [()]                 0           tf_op_layer_AddV2_58[0][0]       
                                                                 tf_op_layer_Mul_88[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_29 (TensorFlowO [(None,)]            0           tf_op_layer_Square_119[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_89 (TensorFlowO [()]                 0           tf_op_layer_Sub_148[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_59 (TensorFlo [(None,)]            0           tf_op_layer_Sum_29[0][0]         
                                                                 tf_op_layer_Mul_89[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_209 (TensorFlo [()]                 0           tf_op_layer_AddV2_59[0][0]       
__________________________________________________________________________________________________
add_loss_29 (AddLoss)           ()                   0           tf_op_layer_Mean_209[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.8143WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1094s). Check your callbacks.
24/24 [==============================] - 0s 15ms/step - loss: 7.8008 - val_loss: 5.1913
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.3828 - val_loss: 3.5473
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.9556 - val_loss: 2.4213
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.2373 - val_loss: 2.0343
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.9456 - val_loss: 1.7972
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8515 - val_loss: 1.6554
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7267 - val_loss: 1.6296
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6585 - val_loss: 1.6175
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5916 - val_loss: 1.5027
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5306 - val_loss: 1.4697
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5175 - val_loss: 1.4272
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4575 - val_loss: 1.3645
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4034 - val_loss: 1.2962
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3585 - val_loss: 1.2359
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3032 - val_loss: 1.1858
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2540 - val_loss: 1.1748
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2170 - val_loss: 1.1766
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2049 - val_loss: 1.1000
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1830 - val_loss: 1.1277
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1589 - val_loss: 1.0999
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1735 - val_loss: 1.0734
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1461 - val_loss: 1.1056
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1267 - val_loss: 1.0763
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1287 - val_loss: 1.0910
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1369 - val_loss: 1.0733
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1200 - val_loss: 1.0984
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1059 - val_loss: 1.0277
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1254 - val_loss: 1.0414
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0940 - val_loss: 1.0578
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1232 - val_loss: 1.0577
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0852 - val_loss: 1.0454
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0955 - val_loss: 1.0424
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0995 - val_loss: 1.0806
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0771 - val_loss: 1.0439
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0728 - val_loss: 1.0229
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0934 - val_loss: 1.0160
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0733 - val_loss: 1.0346
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0709 - val_loss: 1.0138
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0629 - val_loss: 1.0234
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0567 - val_loss: 1.0011
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0633 - val_loss: 1.0147
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0484 - val_loss: 1.0464
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0755 - val_loss: 1.0428
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0582 - val_loss: 0.9892
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0459 - val_loss: 1.0042
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0553 - val_loss: 1.0108
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0516 - val_loss: 0.9631
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0485 - val_loss: 1.0013
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0395 - val_loss: 0.9931
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0291 - val_loss: 1.0301
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0483 - val_loss: 1.0059
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0407 - val_loss: 0.9875
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0397 - val_loss: 0.9868
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0397 - val_loss: 1.0011
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0364 - val_loss: 0.9749
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0335 - val_loss: 1.0187
Epoch 57/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0327 - val_loss: 1.0120
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0326 - val_loss: 1.0301
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0336 - val_loss: 0.9984
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0362 - val_loss: 0.9662
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0329 - val_loss: 1.0201
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0239 - val_loss: 0.9518
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0207 - val_loss: 0.9584
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0252 - val_loss: 1.0001
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0249 - val_loss: 0.9620
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0167 - val_loss: 0.9507
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0175 - val_loss: 0.9824
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0129 - val_loss: 0.9895
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0069 - val_loss: 0.9939
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0131 - val_loss: 0.9622
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0175 - val_loss: 1.0098
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0135 - val_loss: 0.9701
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0294 - val_loss: 0.9465
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0137 - val_loss: 0.9303
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0087 - val_loss: 0.9866
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0375 - val_loss: 1.0072
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0150 - val_loss: 0.9478
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0032 - val_loss: 0.9717
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0132 - val_loss: 0.9580
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0128 - val_loss: 0.9749
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0060 - val_loss: 0.9604
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0096 - val_loss: 0.9351
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0105 - val_loss: 0.9451
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0055 - val_loss: 0.9761
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0097 - val_loss: 0.9642
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0051 - val_loss: 0.9514
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0163 - val_loss: 0.9764
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0032 - val_loss: 0.9564
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0004 - val_loss: 0.9896
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0242 - val_loss: 0.9681
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9915 - val_loss: 0.9894
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9803 - val_loss: 0.9510
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9994 - val_loss: 0.9398
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0047 - val_loss: 0.9503
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9853 - val_loss: 0.9529
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9990 - val_loss: 0.9679
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0079 - val_loss: 0.9864
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9958 - val_loss: 0.9621
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9865 - val_loss: 0.9295
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0079 - val_loss: 0.9429
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.55 seconds.
    Calculating affinities...
    Calculated affinities in 0.06 seconds.
  Calculated graph and diffusion operator in 0.62 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 6.72 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.39 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 10.47 seconds.
Calculated PHATE in 18.22 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_90 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_91 (Dense)                (None, 32)           2080        dense_90[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_91[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_91[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_92 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_93 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_94 (Dense)             (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_90 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_91 (Dense)                (None, 32)           2080        dense_90[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_91[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_91[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_300 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_300 ( [()]                 0           tf_op_layer_Shape_300[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_30 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_300[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_30[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_90 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_30 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_90[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_301 (TensorFl [(2,)]               0           tf_op_layer_Add_30[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_303 (TensorFl [(2,)]               0           tf_op_layer_Add_30[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_302 (TensorFl [(2,)]               0           tf_op_layer_Add_30[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_304 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_306 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_305 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_307 (TensorFl [(2,)]               0           tf_op_layer_Add_30[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_309 (TensorFl [(2,)]               0           tf_op_layer_Add_30[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_308 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_301 ( [()]                 0           tf_op_layer_Shape_301[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_303 ( [()]                 0           tf_op_layer_Shape_303[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_302 ( [()]                 0           tf_op_layer_Shape_302[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_304 ( [()]                 0           tf_op_layer_Shape_304[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_306 ( [()]                 0           tf_op_layer_Shape_306[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_305 ( [()]                 0           tf_op_layer_Shape_305[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_307 ( [()]                 0           tf_op_layer_Shape_307[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_309 ( [()]                 0           tf_op_layer_Shape_309[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_308 ( [()]                 0           tf_op_layer_Shape_308[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_180/shape ( [(3,)]               0           tf_op_layer_strided_slice_301[0][
                                                                 tf_op_layer_strided_slice_303[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_181/shape ( [(3,)]               0           tf_op_layer_strided_slice_302[0][
                                                                 tf_op_layer_strided_slice_303[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_182/shape ( [(3,)]               0           tf_op_layer_strided_slice_304[0][
                                                                 tf_op_layer_strided_slice_306[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_183/shape ( [(3,)]               0           tf_op_layer_strided_slice_305[0][
                                                                 tf_op_layer_strided_slice_306[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_184/shape ( [(3,)]               0           tf_op_layer_strided_slice_307[0][
                                                                 tf_op_layer_strided_slice_309[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_185/shape ( [(3,)]               0           tf_op_layer_strided_slice_308[0][
                                                                 tf_op_layer_strided_slice_309[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_180 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_30[0][0]         
                                                                 tf_op_layer_Reshape_180/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_180/multiples  [(3,)]               0           tf_op_layer_strided_slice_302[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_181 (Tensor [(1, None, None)]    0           tf_op_layer_Add_30[0][0]         
                                                                 tf_op_layer_Reshape_181/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_181/multiples  [(3,)]               0           tf_op_layer_strided_slice_301[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_182 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_182/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_182/multiples  [(3,)]               0           tf_op_layer_strided_slice_305[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_183 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_183/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_183/multiples  [(3,)]               0           tf_op_layer_strided_slice_304[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_184 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_30[0][0]         
                                                                 tf_op_layer_Reshape_184/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_184/multiples  [(3,)]               0           tf_op_layer_strided_slice_308[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_185 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_185/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_185/multiples  [(3,)]               0           tf_op_layer_strided_slice_307[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_180 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_180[0][0]    
                                                                 tf_op_layer_Tile_180/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_181 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_181[0][0]    
                                                                 tf_op_layer_Tile_181/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_182 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_182[0][0]    
                                                                 tf_op_layer_Tile_182/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_183 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_183[0][0]    
                                                                 tf_op_layer_Tile_183/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_184 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_184[0][0]    
                                                                 tf_op_layer_Tile_184/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_185 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_185[0][0]    
                                                                 tf_op_layer_Tile_185/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_150 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_180[0][0]       
                                                                 tf_op_layer_Tile_181[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_151 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_182[0][0]       
                                                                 tf_op_layer_Tile_183[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_152 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_184[0][0]       
                                                                 tf_op_layer_Tile_185[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_120 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_150[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_121 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_151[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_122 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_152[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_210 (TensorFlo [(None, None)]       0           tf_op_layer_Square_120[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_211 (TensorFlo [(None, None)]       0           tf_op_layer_Square_121[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_212 (TensorFlo [(None, None)]       0           tf_op_layer_Square_122[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_90 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_210[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_90 (TensorFlow [()]                 0           tf_op_layer_strided_slice_303[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_91 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_211[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_91 (TensorFlow [()]                 0           tf_op_layer_strided_slice_306[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_92 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_212[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_92 (TensorFlow [()]                 0           tf_op_layer_strided_slice_309[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_90 (TensorF [(None, None)]       0           tf_op_layer_Neg_90[0][0]         
                                                                 tf_op_layer_Cast_90[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_91 (TensorF [(None, None)]       0           tf_op_layer_Neg_91[0][0]         
                                                                 tf_op_layer_Cast_91[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_92 (TensorF [(None, None)]       0           tf_op_layer_Neg_92[0][0]         
                                                                 tf_op_layer_Cast_92[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_90 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_90[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_91 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_91[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_92 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_92[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_213 (TensorFlo [()]                 0           tf_op_layer_Exp_90[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_214 (TensorFlo [()]                 0           tf_op_layer_Exp_91[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_215 (TensorFlo [()]                 0           tf_op_layer_Exp_92[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_154 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_60 (TensorFlo [()]                 0           tf_op_layer_Mean_213[0][0]       
                                                                 tf_op_layer_Mean_214[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_91 (TensorFlowO [()]                 0           tf_op_layer_Mean_215[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_123 (TensorF [(None, 97)]         0           tf_op_layer_Sub_154[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_153 (TensorFlow [()]                 0           tf_op_layer_AddV2_60[0][0]       
                                                                 tf_op_layer_Mul_91[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_30 (TensorFlowO [(None,)]            0           tf_op_layer_Square_123[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_92 (TensorFlowO [()]                 0           tf_op_layer_Sub_153[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_61 (TensorFlo [(None,)]            0           tf_op_layer_Sum_30[0][0]         
                                                                 tf_op_layer_Mul_92[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_216 (TensorFlo [()]                 0           tf_op_layer_AddV2_61[0][0]       
__________________________________________________________________________________________________
add_loss_30 (AddLoss)           ()                   0           tf_op_layer_Mean_216[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.2138WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0019s vs `on_train_batch_end` time: 0.2435s). Check your callbacks.
24/24 [==============================] - 1s 31ms/step - loss: 4.6645 - val_loss: 2.2026
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5801 - val_loss: 1.6328
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2576 - val_loss: 1.5562
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2371 - val_loss: 1.4480
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1326 - val_loss: 1.3401
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0509 - val_loss: 1.2002
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9555 - val_loss: 1.0968
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9021 - val_loss: 1.0308
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8607 - val_loss: 0.9989
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8325 - val_loss: 0.9785
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8390 - val_loss: 0.9701
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8203 - val_loss: 0.9725
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8142 - val_loss: 0.9537
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8147 - val_loss: 0.9427
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8009 - val_loss: 0.9482
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8067 - val_loss: 0.9191
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7889 - val_loss: 0.9094
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7826 - val_loss: 0.9334
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7833 - val_loss: 0.9047
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7786 - val_loss: 0.9307
Epoch 21/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7707 - val_loss: 0.9121
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7705 - val_loss: 0.8696
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7647 - val_loss: 0.8693
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7635 - val_loss: 0.8749
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7665 - val_loss: 0.8895
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7588 - val_loss: 0.8826
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7625 - val_loss: 0.8821
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7430 - val_loss: 0.8715
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7488 - val_loss: 0.8762
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7729 - val_loss: 0.8915
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7582 - val_loss: 0.8897
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7412 - val_loss: 0.8574
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7572 - val_loss: 0.8551
Epoch 34/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7314 - val_loss: 0.8407
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7595 - val_loss: 0.8706
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7414 - val_loss: 0.8723
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7350 - val_loss: 0.8448
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7262 - val_loss: 0.8818
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7371 - val_loss: 0.8661
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7406 - val_loss: 0.8624
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7317 - val_loss: 0.8923
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7335 - val_loss: 0.8895
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7341 - val_loss: 0.8604
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7305 - val_loss: 0.8497
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7306 - val_loss: 0.8631
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7337 - val_loss: 0.8581
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7324 - val_loss: 0.8477
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7288 - val_loss: 0.8604
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7398 - val_loss: 0.8724
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7224 - val_loss: 0.8579
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7225 - val_loss: 0.8516
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7227 - val_loss: 0.8557
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7164 - val_loss: 0.8389
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7204 - val_loss: 0.8423
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7211 - val_loss: 0.8599
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7248 - val_loss: 0.8227
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7174 - val_loss: 0.8736
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7194 - val_loss: 0.8327
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7167 - val_loss: 0.8350
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7255 - val_loss: 0.8701
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7146 - val_loss: 0.8261
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7322 - val_loss: 0.8583
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7176 - val_loss: 0.8898
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7237 - val_loss: 0.8386
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7118 - val_loss: 0.8369
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7145 - val_loss: 0.8642
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7232 - val_loss: 0.8649
Epoch 68/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7075 - val_loss: 0.8238
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7168 - val_loss: 0.8348
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7228 - val_loss: 0.8328
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7220 - val_loss: 0.8270
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7244 - val_loss: 0.8492
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7088 - val_loss: 0.8404
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7072 - val_loss: 0.8181
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7055 - val_loss: 0.8360
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7050 - val_loss: 0.8468
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7153 - val_loss: 0.8316
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7171 - val_loss: 0.8181
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7140 - val_loss: 0.8255
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7094 - val_loss: 0.8789
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7038 - val_loss: 0.8432
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6978 - val_loss: 0.8573
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7017 - val_loss: 0.8331
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7139 - val_loss: 0.8232
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7089 - val_loss: 0.8237
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7085 - val_loss: 0.8306
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7102 - val_loss: 0.8188
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7050 - val_loss: 0.8367
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7013 - val_loss: 0.8444
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6992 - val_loss: 0.8214
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7025 - val_loss: 0.8186
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7002 - val_loss: 0.8336
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6942 - val_loss: 0.8282
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7022 - val_loss: 0.8560
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6986 - val_loss: 0.8484
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6968 - val_loss: 0.8026
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7024 - val_loss: 0.8142
Epoch 98/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6989 - val_loss: 0.8419
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6963 - val_loss: 0.8897
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6967 - val_loss: 0.8196
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_95 (Dense)             (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_310 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_310 ( [()]                 0           tf_op_layer_Shape_310[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_31 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_310[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_31[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_93 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_31 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_93[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_311 (TensorFl [(2,)]               0           tf_op_layer_Add_31[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_313 (TensorFl [(2,)]               0           tf_op_layer_Add_31[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_312 (TensorFl [(2,)]               0           tf_op_layer_Add_31[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_314 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_316 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_315 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_317 (TensorFl [(2,)]               0           tf_op_layer_Add_31[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_319 (TensorFl [(2,)]               0           tf_op_layer_Add_31[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_318 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_311 ( [()]                 0           tf_op_layer_Shape_311[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_313 ( [()]                 0           tf_op_layer_Shape_313[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_312 ( [()]                 0           tf_op_layer_Shape_312[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_314 ( [()]                 0           tf_op_layer_Shape_314[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_316 ( [()]                 0           tf_op_layer_Shape_316[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_315 ( [()]                 0           tf_op_layer_Shape_315[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_317 ( [()]                 0           tf_op_layer_Shape_317[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_319 ( [()]                 0           tf_op_layer_Shape_319[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_318 ( [()]                 0           tf_op_layer_Shape_318[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_186/shape ( [(3,)]               0           tf_op_layer_strided_slice_311[0][
                                                                 tf_op_layer_strided_slice_313[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_187/shape ( [(3,)]               0           tf_op_layer_strided_slice_312[0][
                                                                 tf_op_layer_strided_slice_313[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_188/shape ( [(3,)]               0           tf_op_layer_strided_slice_314[0][
                                                                 tf_op_layer_strided_slice_316[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_189/shape ( [(3,)]               0           tf_op_layer_strided_slice_315[0][
                                                                 tf_op_layer_strided_slice_316[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_190/shape ( [(3,)]               0           tf_op_layer_strided_slice_317[0][
                                                                 tf_op_layer_strided_slice_319[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_191/shape ( [(3,)]               0           tf_op_layer_strided_slice_318[0][
                                                                 tf_op_layer_strided_slice_319[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_186 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_31[0][0]         
                                                                 tf_op_layer_Reshape_186/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_186/multiples  [(3,)]               0           tf_op_layer_strided_slice_312[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_187 (Tensor [(1, None, None)]    0           tf_op_layer_Add_31[0][0]         
                                                                 tf_op_layer_Reshape_187/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_187/multiples  [(3,)]               0           tf_op_layer_strided_slice_311[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_188 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_188/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_188/multiples  [(3,)]               0           tf_op_layer_strided_slice_315[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_189 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_189/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_189/multiples  [(3,)]               0           tf_op_layer_strided_slice_314[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_190 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_31[0][0]         
                                                                 tf_op_layer_Reshape_190/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_190/multiples  [(3,)]               0           tf_op_layer_strided_slice_318[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_191 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_191/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_191/multiples  [(3,)]               0           tf_op_layer_strided_slice_317[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_186 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_186[0][0]    
                                                                 tf_op_layer_Tile_186/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_187 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_187[0][0]    
                                                                 tf_op_layer_Tile_187/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_188 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_188[0][0]    
                                                                 tf_op_layer_Tile_188/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_189 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_189[0][0]    
                                                                 tf_op_layer_Tile_189/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_190 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_190[0][0]    
                                                                 tf_op_layer_Tile_190/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_191 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_191[0][0]    
                                                                 tf_op_layer_Tile_191/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_155 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_186[0][0]       
                                                                 tf_op_layer_Tile_187[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_156 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_188[0][0]       
                                                                 tf_op_layer_Tile_189[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_157 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_190[0][0]       
                                                                 tf_op_layer_Tile_191[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_124 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_155[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_125 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_156[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_126 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_157[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_217 (TensorFlo [(None, None)]       0           tf_op_layer_Square_124[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_218 (TensorFlo [(None, None)]       0           tf_op_layer_Square_125[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_219 (TensorFlo [(None, None)]       0           tf_op_layer_Square_126[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_93 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_217[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_93 (TensorFlow [()]                 0           tf_op_layer_strided_slice_313[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_94 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_218[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_94 (TensorFlow [()]                 0           tf_op_layer_strided_slice_316[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_95 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_219[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_95 (TensorFlow [()]                 0           tf_op_layer_strided_slice_319[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_93 (TensorF [(None, None)]       0           tf_op_layer_Neg_93[0][0]         
                                                                 tf_op_layer_Cast_93[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_94 (TensorF [(None, None)]       0           tf_op_layer_Neg_94[0][0]         
                                                                 tf_op_layer_Cast_94[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_95 (TensorF [(None, None)]       0           tf_op_layer_Neg_95[0][0]         
                                                                 tf_op_layer_Cast_95[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_93 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_93[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_94 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_94[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_95 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_95[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_220 (TensorFlo [()]                 0           tf_op_layer_Exp_93[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_221 (TensorFlo [()]                 0           tf_op_layer_Exp_94[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_222 (TensorFlo [()]                 0           tf_op_layer_Exp_95[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_159 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_62 (TensorFlo [()]                 0           tf_op_layer_Mean_220[0][0]       
                                                                 tf_op_layer_Mean_221[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_94 (TensorFlowO [()]                 0           tf_op_layer_Mean_222[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_127 (TensorF [(None, 97)]         0           tf_op_layer_Sub_159[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_158 (TensorFlow [()]                 0           tf_op_layer_AddV2_62[0][0]       
                                                                 tf_op_layer_Mul_94[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_31 (TensorFlowO [(None,)]            0           tf_op_layer_Square_127[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_95 (TensorFlowO [()]                 0           tf_op_layer_Sub_158[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_63 (TensorFlo [(None,)]            0           tf_op_layer_Sum_31[0][0]         
                                                                 tf_op_layer_Mul_95[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_223 (TensorFlo [()]                 0           tf_op_layer_AddV2_63[0][0]       
__________________________________________________________________________________________________
add_loss_31 (AddLoss)           ()                   0           tf_op_layer_Mean_223[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 3s - loss: 10.4531WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.2817s). Check your callbacks.
24/24 [==============================] - 1s 29ms/step - loss: 8.0741 - val_loss: 5.6563
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 3.5955 - val_loss: 3.0040
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 2.2763 - val_loss: 2.5127
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.9510 - val_loss: 2.2604
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8019 - val_loss: 2.0568
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6733 - val_loss: 1.9253
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5900 - val_loss: 1.8783
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5117 - val_loss: 1.7704
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4458 - val_loss: 1.6376
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4007 - val_loss: 1.6508
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3454 - val_loss: 1.5669
Epoch 12/100
24/24 [==============================] - 0s 4ms/step - loss: 1.3098 - val_loss: 1.5960
Epoch 13/100
24/24 [==============================] - 0s 5ms/step - loss: 1.2562 - val_loss: 1.4549
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2352 - val_loss: 1.4308
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1918 - val_loss: 1.4683
Epoch 16/100
24/24 [==============================] - 0s 8ms/step - loss: 1.1937 - val_loss: 1.4244
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1690 - val_loss: 1.4560
Epoch 18/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1662 - val_loss: 1.3681
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1578 - val_loss: 1.4141
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1293 - val_loss: 1.3866
Epoch 21/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1209 - val_loss: 1.3683
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1350 - val_loss: 1.3699
Epoch 23/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1327 - val_loss: 1.3663
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1081 - val_loss: 1.3703
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1042 - val_loss: 1.3436
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0960 - val_loss: 1.3506
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0794 - val_loss: 1.3297
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0820 - val_loss: 1.2711
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0865 - val_loss: 1.3047
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0810 - val_loss: 1.2518
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0680 - val_loss: 1.3342
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0841 - val_loss: 1.3274
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0726 - val_loss: 1.3038
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0613 - val_loss: 1.2900
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0533 - val_loss: 1.2858
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0646 - val_loss: 1.2717
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0438 - val_loss: 1.2703
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0512 - val_loss: 1.2991
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0434 - val_loss: 1.2693
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0390 - val_loss: 1.3254
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0446 - val_loss: 1.3051
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0452 - val_loss: 1.3105
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0357 - val_loss: 1.3157
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0351 - val_loss: 1.2938
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0472 - val_loss: 1.3015
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0306 - val_loss: 1.2451
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0380 - val_loss: 1.2782
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0270 - val_loss: 1.2644
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0234 - val_loss: 1.2273
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0136 - val_loss: 1.2549
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0259 - val_loss: 1.2347
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0206 - val_loss: 1.2565
Epoch 53/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0135 - val_loss: 1.2309
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0156 - val_loss: 1.2562
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0165 - val_loss: 1.2602
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0112 - val_loss: 1.2689
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0127 - val_loss: 1.2498
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0127 - val_loss: 1.2317
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0080 - val_loss: 1.2357
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9961 - val_loss: 1.2130
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0122 - val_loss: 1.1990
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0162 - val_loss: 1.2219
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0141 - val_loss: 1.2280
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0198 - val_loss: 1.2498
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9917 - val_loss: 1.2938
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9997 - val_loss: 1.2040
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9867 - val_loss: 1.2029
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9963 - val_loss: 1.2231
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9918 - val_loss: 1.2215
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0019 - val_loss: 1.2263
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0109 - val_loss: 1.2328
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9879 - val_loss: 1.1990
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9968 - val_loss: 1.1913
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9846 - val_loss: 1.1686
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9956 - val_loss: 1.1584
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9998 - val_loss: 1.2188
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9851 - val_loss: 1.2083
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9923 - val_loss: 1.2077
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9724 - val_loss: 1.2346
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9864 - val_loss: 1.2245
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9741 - val_loss: 1.2675
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9795 - val_loss: 1.2362
Epoch 83/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9862 - val_loss: 1.1911
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9839 - val_loss: 1.1704
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9897 - val_loss: 1.1619
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9774 - val_loss: 1.2104
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9848 - val_loss: 1.2397
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9752 - val_loss: 1.2056
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9749 - val_loss: 1.1982
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9752 - val_loss: 1.2305
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9795 - val_loss: 1.1946
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9667 - val_loss: 1.1812
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9753 - val_loss: 1.2007
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9674 - val_loss: 1.2329
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9781 - val_loss: 1.2284
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9741 - val_loss: 1.2145
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9790 - val_loss: 1.2149
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9717 - val_loss: 1.1975
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9669 - val_loss: 1.1673
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9634 - val_loss: 1.2301
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.38 seconds.
    Calculating affinities...
    Calculated affinities in 0.02 seconds.
  Calculated graph and diffusion operator in 0.41 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 4.37 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.35 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 9.24 seconds.
Calculated PHATE in 14.39 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_96 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_97 (Dense)                (None, 32)           2080        dense_96[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_97[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_97[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_98 (Dense)             (None, 32)                128       
_________________________________________________________________
dense_99 (Dense)             (None, 64)                2112      
_________________________________________________________________
dense_100 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_96 (Dense)                (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_97 (Dense)                (None, 32)           2080        dense_96[0][0]                   
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_97[0][0]                   
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_97[0][0]                   
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_320 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_320 ( [()]                 0           tf_op_layer_Shape_320[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_32 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_320[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_32[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_96 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_32 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_96[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_321 (TensorFl [(2,)]               0           tf_op_layer_Add_32[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_323 (TensorFl [(2,)]               0           tf_op_layer_Add_32[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_322 (TensorFl [(2,)]               0           tf_op_layer_Add_32[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_324 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_326 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_325 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_327 (TensorFl [(2,)]               0           tf_op_layer_Add_32[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_329 (TensorFl [(2,)]               0           tf_op_layer_Add_32[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_328 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_321 ( [()]                 0           tf_op_layer_Shape_321[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_323 ( [()]                 0           tf_op_layer_Shape_323[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_322 ( [()]                 0           tf_op_layer_Shape_322[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_324 ( [()]                 0           tf_op_layer_Shape_324[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_326 ( [()]                 0           tf_op_layer_Shape_326[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_325 ( [()]                 0           tf_op_layer_Shape_325[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_327 ( [()]                 0           tf_op_layer_Shape_327[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_329 ( [()]                 0           tf_op_layer_Shape_329[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_328 ( [()]                 0           tf_op_layer_Shape_328[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_192/shape ( [(3,)]               0           tf_op_layer_strided_slice_321[0][
                                                                 tf_op_layer_strided_slice_323[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_193/shape ( [(3,)]               0           tf_op_layer_strided_slice_322[0][
                                                                 tf_op_layer_strided_slice_323[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_194/shape ( [(3,)]               0           tf_op_layer_strided_slice_324[0][
                                                                 tf_op_layer_strided_slice_326[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_195/shape ( [(3,)]               0           tf_op_layer_strided_slice_325[0][
                                                                 tf_op_layer_strided_slice_326[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_196/shape ( [(3,)]               0           tf_op_layer_strided_slice_327[0][
                                                                 tf_op_layer_strided_slice_329[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_197/shape ( [(3,)]               0           tf_op_layer_strided_slice_328[0][
                                                                 tf_op_layer_strided_slice_329[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_192 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_32[0][0]         
                                                                 tf_op_layer_Reshape_192/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_192/multiples  [(3,)]               0           tf_op_layer_strided_slice_322[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_193 (Tensor [(1, None, None)]    0           tf_op_layer_Add_32[0][0]         
                                                                 tf_op_layer_Reshape_193/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_193/multiples  [(3,)]               0           tf_op_layer_strided_slice_321[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_194 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_194/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_194/multiples  [(3,)]               0           tf_op_layer_strided_slice_325[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_195 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_195/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_195/multiples  [(3,)]               0           tf_op_layer_strided_slice_324[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_196 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_32[0][0]         
                                                                 tf_op_layer_Reshape_196/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_196/multiples  [(3,)]               0           tf_op_layer_strided_slice_328[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_197 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_197/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_197/multiples  [(3,)]               0           tf_op_layer_strided_slice_327[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_192 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_192[0][0]    
                                                                 tf_op_layer_Tile_192/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_193 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_193[0][0]    
                                                                 tf_op_layer_Tile_193/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_194 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_194[0][0]    
                                                                 tf_op_layer_Tile_194/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_195 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_195[0][0]    
                                                                 tf_op_layer_Tile_195/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_196 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_196[0][0]    
                                                                 tf_op_layer_Tile_196/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_197 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_197[0][0]    
                                                                 tf_op_layer_Tile_197/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_160 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_192[0][0]       
                                                                 tf_op_layer_Tile_193[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_161 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_194[0][0]       
                                                                 tf_op_layer_Tile_195[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_162 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_196[0][0]       
                                                                 tf_op_layer_Tile_197[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_128 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_160[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_129 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_161[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_130 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_162[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_224 (TensorFlo [(None, None)]       0           tf_op_layer_Square_128[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_225 (TensorFlo [(None, None)]       0           tf_op_layer_Square_129[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_226 (TensorFlo [(None, None)]       0           tf_op_layer_Square_130[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_96 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_224[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_96 (TensorFlow [()]                 0           tf_op_layer_strided_slice_323[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_97 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_225[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_97 (TensorFlow [()]                 0           tf_op_layer_strided_slice_326[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_98 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_226[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_98 (TensorFlow [()]                 0           tf_op_layer_strided_slice_329[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_96 (TensorF [(None, None)]       0           tf_op_layer_Neg_96[0][0]         
                                                                 tf_op_layer_Cast_96[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_97 (TensorF [(None, None)]       0           tf_op_layer_Neg_97[0][0]         
                                                                 tf_op_layer_Cast_97[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_98 (TensorF [(None, None)]       0           tf_op_layer_Neg_98[0][0]         
                                                                 tf_op_layer_Cast_98[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Exp_96 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_96[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_97 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_97[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_98 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_98[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_227 (TensorFlo [()]                 0           tf_op_layer_Exp_96[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_228 (TensorFlo [()]                 0           tf_op_layer_Exp_97[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_229 (TensorFlo [()]                 0           tf_op_layer_Exp_98[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sub_164 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_64 (TensorFlo [()]                 0           tf_op_layer_Mean_227[0][0]       
                                                                 tf_op_layer_Mean_228[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_97 (TensorFlowO [()]                 0           tf_op_layer_Mean_229[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_131 (TensorF [(None, 97)]         0           tf_op_layer_Sub_164[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_163 (TensorFlow [()]                 0           tf_op_layer_AddV2_64[0][0]       
                                                                 tf_op_layer_Mul_97[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Sum_32 (TensorFlowO [(None,)]            0           tf_op_layer_Square_131[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_98 (TensorFlowO [()]                 0           tf_op_layer_Sub_163[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_65 (TensorFlo [(None,)]            0           tf_op_layer_Sum_32[0][0]         
                                                                 tf_op_layer_Mul_98[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_230 (TensorFlo [()]                 0           tf_op_layer_AddV2_65[0][0]       
__________________________________________________________________________________________________
add_loss_32 (AddLoss)           ()                   0           tf_op_layer_Mean_230[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.2653WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.1303s). Check your callbacks.
24/24 [==============================] - 0s 17ms/step - loss: 5.2133 - val_loss: 3.0463
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7830 - val_loss: 1.4605
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2935 - val_loss: 1.3460
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2287 - val_loss: 1.3362
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2335 - val_loss: 1.2029
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1121 - val_loss: 1.1570
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0091 - val_loss: 1.0682
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9313 - val_loss: 0.9875
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8777 - val_loss: 0.9426
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8651 - val_loss: 0.9577
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8459 - val_loss: 0.9498
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8423 - val_loss: 0.9669
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8163 - val_loss: 0.8991
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8064 - val_loss: 0.9061
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8169 - val_loss: 0.8925
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8051 - val_loss: 0.8748
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8009 - val_loss: 0.8854
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7972 - val_loss: 0.8918
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7937 - val_loss: 0.8545
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7827 - val_loss: 0.9093
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7835 - val_loss: 0.9366
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7906 - val_loss: 0.8724
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7793 - val_loss: 0.8633
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7710 - val_loss: 0.8767
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7700 - val_loss: 0.8292
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7626 - val_loss: 0.8514
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7591 - val_loss: 0.8774
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7602 - val_loss: 0.8558
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7528 - val_loss: 0.8201
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7631 - val_loss: 0.8279
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7507 - val_loss: 0.8171
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7466 - val_loss: 0.8082
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7393 - val_loss: 0.8323
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7443 - val_loss: 0.8409
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7434 - val_loss: 0.8242
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7398 - val_loss: 0.8207
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7347 - val_loss: 0.8511
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7412 - val_loss: 0.8452
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7473 - val_loss: 0.8341
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7264 - val_loss: 0.8302
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7351 - val_loss: 0.8294
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7389 - val_loss: 0.8337
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7254 - val_loss: 0.7876
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7277 - val_loss: 0.7916
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7273 - val_loss: 0.8106
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7363 - val_loss: 0.8026
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7108 - val_loss: 0.7802
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7232 - val_loss: 0.7711
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7093 - val_loss: 0.7996
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7157 - val_loss: 0.7895
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7140 - val_loss: 0.8400
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7189 - val_loss: 0.7879
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7107 - val_loss: 0.8345
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7162 - val_loss: 0.8022
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7119 - val_loss: 0.8026
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7071 - val_loss: 0.7779
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7119 - val_loss: 0.8000
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7147 - val_loss: 0.7988
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7113 - val_loss: 0.8103
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6991 - val_loss: 0.8198
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7013 - val_loss: 0.7584
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7025 - val_loss: 0.7895
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7083 - val_loss: 0.8252
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7246 - val_loss: 0.8041
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7034 - val_loss: 0.7850
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7053 - val_loss: 0.7940
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7046 - val_loss: 0.7787
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6960 - val_loss: 0.8101
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7060 - val_loss: 0.8493
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7078 - val_loss: 0.7859
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6943 - val_loss: 0.8086
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6958 - val_loss: 0.7987
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6959 - val_loss: 0.7876
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7053 - val_loss: 0.7865
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6998 - val_loss: 0.8088
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6893 - val_loss: 0.7933
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6978 - val_loss: 0.7782
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6974 - val_loss: 0.7802
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7018 - val_loss: 0.7722
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6915 - val_loss: 0.8166
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6866 - val_loss: 0.7963
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7051 - val_loss: 0.7733
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6958 - val_loss: 0.7566
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6869 - val_loss: 0.7963
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6836 - val_loss: 0.7868
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6883 - val_loss: 0.7912
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6808 - val_loss: 0.7920
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6836 - val_loss: 0.7709
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6832 - val_loss: 0.7763
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6841 - val_loss: 0.8195
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6864 - val_loss: 0.7742
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6923 - val_loss: 0.7555
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6924 - val_loss: 0.7707
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6777 - val_loss: 0.7618
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6954 - val_loss: 0.7895
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6815 - val_loss: 0.7587
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6870 - val_loss: 0.8068
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6827 - val_loss: 0.7704
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6824 - val_loss: 0.7784
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6823 - val_loss: 0.7745
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_101 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_330 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_330 ( [()]                 0           tf_op_layer_Shape_330[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_33 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_330[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_33[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_99 (TensorFlowO [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_33 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_99[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_331 (TensorFl [(2,)]               0           tf_op_layer_Add_33[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_333 (TensorFl [(2,)]               0           tf_op_layer_Add_33[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_332 (TensorFl [(2,)]               0           tf_op_layer_Add_33[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_334 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_336 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_335 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_337 (TensorFl [(2,)]               0           tf_op_layer_Add_33[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_339 (TensorFl [(2,)]               0           tf_op_layer_Add_33[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_338 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_331 ( [()]                 0           tf_op_layer_Shape_331[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_333 ( [()]                 0           tf_op_layer_Shape_333[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_332 ( [()]                 0           tf_op_layer_Shape_332[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_334 ( [()]                 0           tf_op_layer_Shape_334[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_336 ( [()]                 0           tf_op_layer_Shape_336[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_335 ( [()]                 0           tf_op_layer_Shape_335[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_337 ( [()]                 0           tf_op_layer_Shape_337[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_339 ( [()]                 0           tf_op_layer_Shape_339[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_338 ( [()]                 0           tf_op_layer_Shape_338[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_198/shape ( [(3,)]               0           tf_op_layer_strided_slice_331[0][
                                                                 tf_op_layer_strided_slice_333[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_199/shape ( [(3,)]               0           tf_op_layer_strided_slice_332[0][
                                                                 tf_op_layer_strided_slice_333[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_200/shape ( [(3,)]               0           tf_op_layer_strided_slice_334[0][
                                                                 tf_op_layer_strided_slice_336[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_201/shape ( [(3,)]               0           tf_op_layer_strided_slice_335[0][
                                                                 tf_op_layer_strided_slice_336[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_202/shape ( [(3,)]               0           tf_op_layer_strided_slice_337[0][
                                                                 tf_op_layer_strided_slice_339[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_203/shape ( [(3,)]               0           tf_op_layer_strided_slice_338[0][
                                                                 tf_op_layer_strided_slice_339[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_198 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_33[0][0]         
                                                                 tf_op_layer_Reshape_198/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_198/multiples  [(3,)]               0           tf_op_layer_strided_slice_332[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_199 (Tensor [(1, None, None)]    0           tf_op_layer_Add_33[0][0]         
                                                                 tf_op_layer_Reshape_199/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_199/multiples  [(3,)]               0           tf_op_layer_strided_slice_331[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_200 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_200/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_200/multiples  [(3,)]               0           tf_op_layer_strided_slice_335[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_201 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_201/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_201/multiples  [(3,)]               0           tf_op_layer_strided_slice_334[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_202 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_33[0][0]         
                                                                 tf_op_layer_Reshape_202/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_202/multiples  [(3,)]               0           tf_op_layer_strided_slice_338[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_203 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_203/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_203/multiples  [(3,)]               0           tf_op_layer_strided_slice_337[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_198 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_198[0][0]    
                                                                 tf_op_layer_Tile_198/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_199 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_199[0][0]    
                                                                 tf_op_layer_Tile_199/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_200 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_200[0][0]    
                                                                 tf_op_layer_Tile_200/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_201 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_201[0][0]    
                                                                 tf_op_layer_Tile_201/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_202 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_202[0][0]    
                                                                 tf_op_layer_Tile_202/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_203 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_203[0][0]    
                                                                 tf_op_layer_Tile_203/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_165 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_198[0][0]       
                                                                 tf_op_layer_Tile_199[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_166 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_200[0][0]       
                                                                 tf_op_layer_Tile_201[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_167 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_202[0][0]       
                                                                 tf_op_layer_Tile_203[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_132 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_165[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_133 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_166[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_134 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_167[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_231 (TensorFlo [(None, None)]       0           tf_op_layer_Square_132[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_232 (TensorFlo [(None, None)]       0           tf_op_layer_Square_133[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_233 (TensorFlo [(None, None)]       0           tf_op_layer_Square_134[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_99 (TensorFlowO [(None, None)]       0           tf_op_layer_Mean_231[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_99 (TensorFlow [()]                 0           tf_op_layer_strided_slice_333[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_100 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_232[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_100 (TensorFlo [()]                 0           tf_op_layer_strided_slice_336[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_101 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_233[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_101 (TensorFlo [()]                 0           tf_op_layer_strided_slice_339[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_99 (TensorF [(None, None)]       0           tf_op_layer_Neg_99[0][0]         
                                                                 tf_op_layer_Cast_99[0][0]        
__________________________________________________________________________________________________
tf_op_layer_RealDiv_100 (Tensor [(None, None)]       0           tf_op_layer_Neg_100[0][0]        
                                                                 tf_op_layer_Cast_100[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_101 (Tensor [(None, None)]       0           tf_op_layer_Neg_101[0][0]        
                                                                 tf_op_layer_Cast_101[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_99 (TensorFlowO [(None, None)]       0           tf_op_layer_RealDiv_99[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Exp_100 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_100[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_101 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_101[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_234 (TensorFlo [()]                 0           tf_op_layer_Exp_99[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Mean_235 (TensorFlo [()]                 0           tf_op_layer_Exp_100[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_236 (TensorFlo [()]                 0           tf_op_layer_Exp_101[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_169 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_66 (TensorFlo [()]                 0           tf_op_layer_Mean_234[0][0]       
                                                                 tf_op_layer_Mean_235[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_100 (TensorFlow [()]                 0           tf_op_layer_Mean_236[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_135 (TensorF [(None, 97)]         0           tf_op_layer_Sub_169[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_168 (TensorFlow [()]                 0           tf_op_layer_AddV2_66[0][0]       
                                                                 tf_op_layer_Mul_100[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_33 (TensorFlowO [(None,)]            0           tf_op_layer_Square_135[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_101 (TensorFlow [()]                 0           tf_op_layer_Sub_168[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_67 (TensorFlo [(None,)]            0           tf_op_layer_Sum_33[0][0]         
                                                                 tf_op_layer_Mul_101[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_237 (TensorFlo [()]                 0           tf_op_layer_AddV2_67[0][0]       
__________________________________________________________________________________________________
add_loss_33 (AddLoss)           ()                   0           tf_op_layer_Mean_237[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.5737WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0013s vs `on_train_batch_end` time: 0.1130s). Check your callbacks.
24/24 [==============================] - 0s 14ms/step - loss: 7.8444 - val_loss: 6.0435
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.1904 - val_loss: 3.1915
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.5678 - val_loss: 2.2385
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.0216 - val_loss: 1.9380
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8528 - val_loss: 1.7525
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7055 - val_loss: 1.6978
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6355 - val_loss: 1.6011
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5383 - val_loss: 1.4549
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4444 - val_loss: 1.4611
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3735 - val_loss: 1.3904
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3558 - val_loss: 1.3232
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3048 - val_loss: 1.2637
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2638 - val_loss: 1.2714
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2463 - val_loss: 1.2454
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2236 - val_loss: 1.1945
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2095 - val_loss: 1.1662
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1721 - val_loss: 1.1311
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1768 - val_loss: 1.1600
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1740 - val_loss: 1.1193
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1675 - val_loss: 1.1383
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1308 - val_loss: 1.1618
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1301 - val_loss: 1.0994
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1173 - val_loss: 1.1182
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1209 - val_loss: 1.1545
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0930 - val_loss: 1.1335
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1029 - val_loss: 1.0579
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0874 - val_loss: 1.1315
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0969 - val_loss: 1.0966
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0800 - val_loss: 1.1026
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0809 - val_loss: 1.1093
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0816 - val_loss: 1.0417
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0735 - val_loss: 1.1167
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0687 - val_loss: 1.0863
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0651 - val_loss: 1.0627
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0782 - val_loss: 1.1066
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0674 - val_loss: 1.0722
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0665 - val_loss: 1.0938
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0549 - val_loss: 1.0548
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0442 - val_loss: 1.0758
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0575 - val_loss: 1.0623
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0531 - val_loss: 1.0406
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0536 - val_loss: 1.0922
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0545 - val_loss: 1.0201
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0583 - val_loss: 1.0584
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0341 - val_loss: 1.0354
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0708 - val_loss: 1.0377
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0265 - val_loss: 1.0785
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0301 - val_loss: 1.0173
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0251 - val_loss: 1.0403
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0190 - val_loss: 1.0440
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0282 - val_loss: 1.0218
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0370 - val_loss: 1.0311
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0365 - val_loss: 1.0164
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0139 - val_loss: 1.0409
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0254 - val_loss: 1.0247
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0281 - val_loss: 1.0487
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0216 - val_loss: 1.0590
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0271 - val_loss: 1.0313
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0276 - val_loss: 1.0247
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0284 - val_loss: 1.0226
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0035 - val_loss: 1.0184
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0121 - val_loss: 1.0318
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0131 - val_loss: 1.0309
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0158 - val_loss: 1.0415
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0006 - val_loss: 1.0224
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0132 - val_loss: 1.0351
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0078 - val_loss: 1.0252
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0119 - val_loss: 1.0342
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9988 - val_loss: 1.0140
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0087 - val_loss: 1.0242
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0010 - val_loss: 1.0266
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9919 - val_loss: 1.0132
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0047 - val_loss: 1.0319
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0024 - val_loss: 0.9984
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0002 - val_loss: 1.0009
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0113 - val_loss: 1.0262
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9971 - val_loss: 1.0745
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9939 - val_loss: 1.0009
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9992 - val_loss: 0.9807
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9998 - val_loss: 1.0210
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9968 - val_loss: 1.0137
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0045 - val_loss: 0.9943
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9921 - val_loss: 1.0250
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9903 - val_loss: 1.0216
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9941 - val_loss: 0.9868
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9898 - val_loss: 1.0064
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9904 - val_loss: 1.0135
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9940 - val_loss: 1.0266
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9931 - val_loss: 1.0216
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9960 - val_loss: 1.0024
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9827 - val_loss: 1.0244
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0060 - val_loss: 1.0284
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9944 - val_loss: 0.9825
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9989 - val_loss: 1.0582
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9770 - val_loss: 0.9925
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9940 - val_loss: 0.9997
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9943 - val_loss: 1.0109
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9893 - val_loss: 1.0128
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0073 - val_loss: 0.9948
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9781 - val_loss: 0.9782
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.32 seconds.
    Calculating affinities...
    Calculated affinities in 0.02 seconds.
  Calculated graph and diffusion operator in 0.35 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 2.05 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.25 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 8.06 seconds.
Calculated PHATE in 10.74 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_102 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_103 (Dense)               (None, 32)           2080        dense_102[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_103[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_103[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_104 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_105 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_106 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_102 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_103 (Dense)               (None, 32)           2080        dense_102[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_103[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_103[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_340 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_340 ( [()]                 0           tf_op_layer_Shape_340[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_34 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_340[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_34[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_102 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_34 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_102[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_341 (TensorFl [(2,)]               0           tf_op_layer_Add_34[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_343 (TensorFl [(2,)]               0           tf_op_layer_Add_34[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_342 (TensorFl [(2,)]               0           tf_op_layer_Add_34[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_344 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_346 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_345 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_347 (TensorFl [(2,)]               0           tf_op_layer_Add_34[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_349 (TensorFl [(2,)]               0           tf_op_layer_Add_34[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_348 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_341 ( [()]                 0           tf_op_layer_Shape_341[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_343 ( [()]                 0           tf_op_layer_Shape_343[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_342 ( [()]                 0           tf_op_layer_Shape_342[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_344 ( [()]                 0           tf_op_layer_Shape_344[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_346 ( [()]                 0           tf_op_layer_Shape_346[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_345 ( [()]                 0           tf_op_layer_Shape_345[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_347 ( [()]                 0           tf_op_layer_Shape_347[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_349 ( [()]                 0           tf_op_layer_Shape_349[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_348 ( [()]                 0           tf_op_layer_Shape_348[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_204/shape ( [(3,)]               0           tf_op_layer_strided_slice_341[0][
                                                                 tf_op_layer_strided_slice_343[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_205/shape ( [(3,)]               0           tf_op_layer_strided_slice_342[0][
                                                                 tf_op_layer_strided_slice_343[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_206/shape ( [(3,)]               0           tf_op_layer_strided_slice_344[0][
                                                                 tf_op_layer_strided_slice_346[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_207/shape ( [(3,)]               0           tf_op_layer_strided_slice_345[0][
                                                                 tf_op_layer_strided_slice_346[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_208/shape ( [(3,)]               0           tf_op_layer_strided_slice_347[0][
                                                                 tf_op_layer_strided_slice_349[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_209/shape ( [(3,)]               0           tf_op_layer_strided_slice_348[0][
                                                                 tf_op_layer_strided_slice_349[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_204 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_34[0][0]         
                                                                 tf_op_layer_Reshape_204/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_204/multiples  [(3,)]               0           tf_op_layer_strided_slice_342[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_205 (Tensor [(1, None, None)]    0           tf_op_layer_Add_34[0][0]         
                                                                 tf_op_layer_Reshape_205/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_205/multiples  [(3,)]               0           tf_op_layer_strided_slice_341[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_206 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_206/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_206/multiples  [(3,)]               0           tf_op_layer_strided_slice_345[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_207 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_207/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_207/multiples  [(3,)]               0           tf_op_layer_strided_slice_344[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_208 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_34[0][0]         
                                                                 tf_op_layer_Reshape_208/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_208/multiples  [(3,)]               0           tf_op_layer_strided_slice_348[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_209 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_209/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_209/multiples  [(3,)]               0           tf_op_layer_strided_slice_347[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_204 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_204[0][0]    
                                                                 tf_op_layer_Tile_204/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_205 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_205[0][0]    
                                                                 tf_op_layer_Tile_205/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_206 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_206[0][0]    
                                                                 tf_op_layer_Tile_206/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_207 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_207[0][0]    
                                                                 tf_op_layer_Tile_207/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_208 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_208[0][0]    
                                                                 tf_op_layer_Tile_208/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_209 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_209[0][0]    
                                                                 tf_op_layer_Tile_209/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_170 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_204[0][0]       
                                                                 tf_op_layer_Tile_205[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_171 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_206[0][0]       
                                                                 tf_op_layer_Tile_207[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_172 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_208[0][0]       
                                                                 tf_op_layer_Tile_209[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_136 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_170[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_137 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_171[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_138 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_172[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_238 (TensorFlo [(None, None)]       0           tf_op_layer_Square_136[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_239 (TensorFlo [(None, None)]       0           tf_op_layer_Square_137[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_240 (TensorFlo [(None, None)]       0           tf_op_layer_Square_138[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_102 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_238[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_102 (TensorFlo [()]                 0           tf_op_layer_strided_slice_343[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_103 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_239[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_103 (TensorFlo [()]                 0           tf_op_layer_strided_slice_346[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_104 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_240[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_104 (TensorFlo [()]                 0           tf_op_layer_strided_slice_349[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_102 (Tensor [(None, None)]       0           tf_op_layer_Neg_102[0][0]        
                                                                 tf_op_layer_Cast_102[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_103 (Tensor [(None, None)]       0           tf_op_layer_Neg_103[0][0]        
                                                                 tf_op_layer_Cast_103[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_104 (Tensor [(None, None)]       0           tf_op_layer_Neg_104[0][0]        
                                                                 tf_op_layer_Cast_104[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_102 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_102[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_103 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_103[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_104 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_104[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_241 (TensorFlo [()]                 0           tf_op_layer_Exp_102[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_242 (TensorFlo [()]                 0           tf_op_layer_Exp_103[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_243 (TensorFlo [()]                 0           tf_op_layer_Exp_104[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_174 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_68 (TensorFlo [()]                 0           tf_op_layer_Mean_241[0][0]       
                                                                 tf_op_layer_Mean_242[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_103 (TensorFlow [()]                 0           tf_op_layer_Mean_243[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_139 (TensorF [(None, 97)]         0           tf_op_layer_Sub_174[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_173 (TensorFlow [()]                 0           tf_op_layer_AddV2_68[0][0]       
                                                                 tf_op_layer_Mul_103[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_34 (TensorFlowO [(None,)]            0           tf_op_layer_Square_139[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_104 (TensorFlow [()]                 0           tf_op_layer_Sub_173[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_69 (TensorFlo [(None,)]            0           tf_op_layer_Sum_34[0][0]         
                                                                 tf_op_layer_Mul_104[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_244 (TensorFlo [()]                 0           tf_op_layer_AddV2_69[0][0]       
__________________________________________________________________________________________________
add_loss_34 (AddLoss)           ()                   0           tf_op_layer_Mean_244[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 9.8834 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0025s vs `on_train_batch_end` time: 0.2258s). Check your callbacks.
24/24 [==============================] - 1s 32ms/step - loss: 4.2350 - val_loss: 1.8174
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4716 - val_loss: 1.8708
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3859 - val_loss: 1.2366
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1128 - val_loss: 1.0918
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9554 - val_loss: 1.0252
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9173 - val_loss: 0.9576
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8783 - val_loss: 0.9350
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8428 - val_loss: 0.9336
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8198 - val_loss: 0.9415
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8256 - val_loss: 0.9031
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8216 - val_loss: 0.9311
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8188 - val_loss: 0.9522
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8059 - val_loss: 0.9164
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7960 - val_loss: 0.8908
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7882 - val_loss: 0.8899
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7711 - val_loss: 0.8572
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7805 - val_loss: 0.8687
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7698 - val_loss: 0.8488
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7755 - val_loss: 0.8780
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7706 - val_loss: 0.8576
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7616 - val_loss: 0.8346
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7677 - val_loss: 0.8319
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7580 - val_loss: 0.8816
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7537 - val_loss: 0.8346
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7512 - val_loss: 0.8202
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7481 - val_loss: 0.8130
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7481 - val_loss: 0.8357
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7385 - val_loss: 0.8785
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7409 - val_loss: 0.8291
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7476 - val_loss: 0.8518
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7322 - val_loss: 0.8191
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7353 - val_loss: 0.8305
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7324 - val_loss: 0.8217
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7256 - val_loss: 0.8150
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7312 - val_loss: 0.8165
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7307 - val_loss: 0.7997
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7118 - val_loss: 0.8102
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7207 - val_loss: 0.8005
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7279 - val_loss: 0.7999
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7204 - val_loss: 0.8178
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7148 - val_loss: 0.8474
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7259 - val_loss: 0.7931
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7229 - val_loss: 0.7955
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7261 - val_loss: 0.7903
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7216 - val_loss: 0.7993
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7191 - val_loss: 0.8220
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7336 - val_loss: 0.8037
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7130 - val_loss: 0.7863
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7135 - val_loss: 0.7816
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7112 - val_loss: 0.7930
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7112 - val_loss: 0.8040
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7010 - val_loss: 0.7955
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7206 - val_loss: 0.7902
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7118 - val_loss: 0.7889
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7171 - val_loss: 0.7887
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7043 - val_loss: 0.7960
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7170 - val_loss: 0.7950
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7039 - val_loss: 0.7982
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7071 - val_loss: 0.7716
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7146 - val_loss: 0.7989
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7123 - val_loss: 0.8227
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7086 - val_loss: 0.7849
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7060 - val_loss: 0.8154
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7042 - val_loss: 0.7915
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7053 - val_loss: 0.8001
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7137 - val_loss: 0.7797
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6996 - val_loss: 0.7967
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6997 - val_loss: 0.8042
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6994 - val_loss: 0.7896
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7050 - val_loss: 0.7891
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7040 - val_loss: 0.8339
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7016 - val_loss: 0.7976
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6929 - val_loss: 0.7774
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6941 - val_loss: 0.7832
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6940 - val_loss: 0.7726
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6924 - val_loss: 0.7978
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6886 - val_loss: 0.8066
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6886 - val_loss: 0.7890
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6974 - val_loss: 0.8122
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7079 - val_loss: 0.7721
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6934 - val_loss: 0.7974
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6980 - val_loss: 0.7751
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6997 - val_loss: 0.7783
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6897 - val_loss: 0.7675
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6880 - val_loss: 0.8155
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7038 - val_loss: 0.7844
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6909 - val_loss: 0.7719
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6889 - val_loss: 0.7920
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6899 - val_loss: 0.7848
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6990 - val_loss: 0.7794
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6899 - val_loss: 0.7915
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7008 - val_loss: 0.7725
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6818 - val_loss: 0.7757
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7012 - val_loss: 0.7884
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6922 - val_loss: 0.7858
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6930 - val_loss: 0.7845
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6932 - val_loss: 0.7751
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6846 - val_loss: 0.7789
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6865 - val_loss: 0.7797
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6899 - val_loss: 0.7650
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_107 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_350 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_350 ( [()]                 0           tf_op_layer_Shape_350[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_35 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_350[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_35[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_105 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_35 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_105[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_351 (TensorFl [(2,)]               0           tf_op_layer_Add_35[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_353 (TensorFl [(2,)]               0           tf_op_layer_Add_35[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_352 (TensorFl [(2,)]               0           tf_op_layer_Add_35[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_354 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_356 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_355 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_357 (TensorFl [(2,)]               0           tf_op_layer_Add_35[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_359 (TensorFl [(2,)]               0           tf_op_layer_Add_35[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_358 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_351 ( [()]                 0           tf_op_layer_Shape_351[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_353 ( [()]                 0           tf_op_layer_Shape_353[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_352 ( [()]                 0           tf_op_layer_Shape_352[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_354 ( [()]                 0           tf_op_layer_Shape_354[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_356 ( [()]                 0           tf_op_layer_Shape_356[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_355 ( [()]                 0           tf_op_layer_Shape_355[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_357 ( [()]                 0           tf_op_layer_Shape_357[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_359 ( [()]                 0           tf_op_layer_Shape_359[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_358 ( [()]                 0           tf_op_layer_Shape_358[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_210/shape ( [(3,)]               0           tf_op_layer_strided_slice_351[0][
                                                                 tf_op_layer_strided_slice_353[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_211/shape ( [(3,)]               0           tf_op_layer_strided_slice_352[0][
                                                                 tf_op_layer_strided_slice_353[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_212/shape ( [(3,)]               0           tf_op_layer_strided_slice_354[0][
                                                                 tf_op_layer_strided_slice_356[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_213/shape ( [(3,)]               0           tf_op_layer_strided_slice_355[0][
                                                                 tf_op_layer_strided_slice_356[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_214/shape ( [(3,)]               0           tf_op_layer_strided_slice_357[0][
                                                                 tf_op_layer_strided_slice_359[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_215/shape ( [(3,)]               0           tf_op_layer_strided_slice_358[0][
                                                                 tf_op_layer_strided_slice_359[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_210 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_35[0][0]         
                                                                 tf_op_layer_Reshape_210/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_210/multiples  [(3,)]               0           tf_op_layer_strided_slice_352[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_211 (Tensor [(1, None, None)]    0           tf_op_layer_Add_35[0][0]         
                                                                 tf_op_layer_Reshape_211/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_211/multiples  [(3,)]               0           tf_op_layer_strided_slice_351[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_212 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_212/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_212/multiples  [(3,)]               0           tf_op_layer_strided_slice_355[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_213 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_213/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_213/multiples  [(3,)]               0           tf_op_layer_strided_slice_354[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_214 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_35[0][0]         
                                                                 tf_op_layer_Reshape_214/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_214/multiples  [(3,)]               0           tf_op_layer_strided_slice_358[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_215 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_215/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_215/multiples  [(3,)]               0           tf_op_layer_strided_slice_357[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_210 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_210[0][0]    
                                                                 tf_op_layer_Tile_210/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_211 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_211[0][0]    
                                                                 tf_op_layer_Tile_211/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_212 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_212[0][0]    
                                                                 tf_op_layer_Tile_212/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_213 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_213[0][0]    
                                                                 tf_op_layer_Tile_213/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_214 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_214[0][0]    
                                                                 tf_op_layer_Tile_214/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_215 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_215[0][0]    
                                                                 tf_op_layer_Tile_215/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_175 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_210[0][0]       
                                                                 tf_op_layer_Tile_211[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_176 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_212[0][0]       
                                                                 tf_op_layer_Tile_213[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_177 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_214[0][0]       
                                                                 tf_op_layer_Tile_215[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_140 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_175[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_141 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_176[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_142 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_177[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_245 (TensorFlo [(None, None)]       0           tf_op_layer_Square_140[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_246 (TensorFlo [(None, None)]       0           tf_op_layer_Square_141[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_247 (TensorFlo [(None, None)]       0           tf_op_layer_Square_142[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_105 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_245[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_105 (TensorFlo [()]                 0           tf_op_layer_strided_slice_353[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_106 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_246[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_106 (TensorFlo [()]                 0           tf_op_layer_strided_slice_356[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_107 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_247[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_107 (TensorFlo [()]                 0           tf_op_layer_strided_slice_359[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_105 (Tensor [(None, None)]       0           tf_op_layer_Neg_105[0][0]        
                                                                 tf_op_layer_Cast_105[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_106 (Tensor [(None, None)]       0           tf_op_layer_Neg_106[0][0]        
                                                                 tf_op_layer_Cast_106[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_107 (Tensor [(None, None)]       0           tf_op_layer_Neg_107[0][0]        
                                                                 tf_op_layer_Cast_107[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_105 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_105[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_106 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_106[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_107 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_107[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_248 (TensorFlo [()]                 0           tf_op_layer_Exp_105[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_249 (TensorFlo [()]                 0           tf_op_layer_Exp_106[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_250 (TensorFlo [()]                 0           tf_op_layer_Exp_107[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_179 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_70 (TensorFlo [()]                 0           tf_op_layer_Mean_248[0][0]       
                                                                 tf_op_layer_Mean_249[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_106 (TensorFlow [()]                 0           tf_op_layer_Mean_250[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_143 (TensorF [(None, 97)]         0           tf_op_layer_Sub_179[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_178 (TensorFlow [()]                 0           tf_op_layer_AddV2_70[0][0]       
                                                                 tf_op_layer_Mul_106[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_35 (TensorFlowO [(None,)]            0           tf_op_layer_Square_143[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_107 (TensorFlow [()]                 0           tf_op_layer_Sub_178[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_71 (TensorFlo [(None,)]            0           tf_op_layer_Sum_35[0][0]         
                                                                 tf_op_layer_Mul_107[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_251 (TensorFlo [()]                 0           tf_op_layer_AddV2_71[0][0]       
__________________________________________________________________________________________________
add_loss_35 (AddLoss)           ()                   0           tf_op_layer_Mean_251[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.5302WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.1471s). Check your callbacks.
24/24 [==============================] - 0s 18ms/step - loss: 7.9942 - val_loss: 6.0258
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.0371 - val_loss: 3.5431
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 2.5055 - val_loss: 2.9120
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.0303 - val_loss: 2.6176
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8205 - val_loss: 2.3730
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6859 - val_loss: 2.3133
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6111 - val_loss: 2.1852
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5018 - val_loss: 2.0769
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4236 - val_loss: 1.9627
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3505 - val_loss: 1.8888
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3184 - val_loss: 1.8607
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2743 - val_loss: 1.7783
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2366 - val_loss: 1.7810
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2225 - val_loss: 1.6936
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2046 - val_loss: 1.6873
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1805 - val_loss: 1.6614
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1604 - val_loss: 1.6242
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1507 - val_loss: 1.6218
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1337 - val_loss: 1.5903
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1184 - val_loss: 1.6128
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1183 - val_loss: 1.6145
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1125 - val_loss: 1.5602
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0996 - val_loss: 1.5667
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1052 - val_loss: 1.5412
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1045 - val_loss: 1.5284
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0914 - val_loss: 1.5877
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0852 - val_loss: 1.5322
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0824 - val_loss: 1.5102
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0739 - val_loss: 1.5608
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0694 - val_loss: 1.5169
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0668 - val_loss: 1.5045
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0725 - val_loss: 1.5018
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0593 - val_loss: 1.5721
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0500 - val_loss: 1.4735
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0717 - val_loss: 1.4536
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0492 - val_loss: 1.5204
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0620 - val_loss: 1.4718
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0442 - val_loss: 1.5206
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0485 - val_loss: 1.4792
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0404 - val_loss: 1.4603
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0193 - val_loss: 1.4980
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0463 - val_loss: 1.5005
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0260 - val_loss: 1.4746
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0355 - val_loss: 1.4488
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0175 - val_loss: 1.4086
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0180 - val_loss: 1.4778
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0275 - val_loss: 1.4681
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0222 - val_loss: 1.4782
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0198 - val_loss: 1.4301
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0079 - val_loss: 1.4688
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0123 - val_loss: 1.4141
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0239 - val_loss: 1.4811
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0146 - val_loss: 1.4531
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0126 - val_loss: 1.4479
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0163 - val_loss: 1.3961
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0250 - val_loss: 1.4419
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0047 - val_loss: 1.4473
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0107 - val_loss: 1.4466
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0047 - val_loss: 1.4198
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0126 - val_loss: 1.4336
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0086 - val_loss: 1.4325
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0014 - val_loss: 1.4319
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0085 - val_loss: 1.4202
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0170 - val_loss: 1.4011
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9938 - val_loss: 1.3842
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9969 - val_loss: 1.4263
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0154 - val_loss: 1.4361
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0075 - val_loss: 1.4369
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0058 - val_loss: 1.4102
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9926 - val_loss: 1.4453
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0036 - val_loss: 1.3964
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9865 - val_loss: 1.3855
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9916 - val_loss: 1.4272
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0116 - val_loss: 1.4334
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9924 - val_loss: 1.3895
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9890 - val_loss: 1.4007
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9940 - val_loss: 1.4384
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9952 - val_loss: 1.4256
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9780 - val_loss: 1.3845
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9933 - val_loss: 1.4038
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9829 - val_loss: 1.4236
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9822 - val_loss: 1.4422
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9928 - val_loss: 1.3841
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9946 - val_loss: 1.3997
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9851 - val_loss: 1.4171
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9960 - val_loss: 1.4071
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9810 - val_loss: 1.3880
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9851 - val_loss: 1.4361
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9756 - val_loss: 1.4185
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9748 - val_loss: 1.3759
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9761 - val_loss: 1.3706
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9803 - val_loss: 1.4273
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9815 - val_loss: 1.3637
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9767 - val_loss: 1.4056
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9807 - val_loss: 1.3806
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9796 - val_loss: 1.4027
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9923 - val_loss: 1.3900
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9826 - val_loss: 1.3712
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9773 - val_loss: 1.3693
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9872 - val_loss: 1.3917
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.29 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.31 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.32 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.23 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 6.69 seconds.
Calculated PHATE in 8.56 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_108 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_109 (Dense)               (None, 32)           2080        dense_108[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_109[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_109[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_110 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_111 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_112 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_108 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_109 (Dense)               (None, 32)           2080        dense_108[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_109[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_109[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_360 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_360 ( [()]                 0           tf_op_layer_Shape_360[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_36 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_360[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_36[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_108 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_36 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_108[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_361 (TensorFl [(2,)]               0           tf_op_layer_Add_36[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_363 (TensorFl [(2,)]               0           tf_op_layer_Add_36[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_362 (TensorFl [(2,)]               0           tf_op_layer_Add_36[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_364 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_366 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_365 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_367 (TensorFl [(2,)]               0           tf_op_layer_Add_36[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_369 (TensorFl [(2,)]               0           tf_op_layer_Add_36[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_368 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_361 ( [()]                 0           tf_op_layer_Shape_361[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_363 ( [()]                 0           tf_op_layer_Shape_363[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_362 ( [()]                 0           tf_op_layer_Shape_362[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_364 ( [()]                 0           tf_op_layer_Shape_364[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_366 ( [()]                 0           tf_op_layer_Shape_366[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_365 ( [()]                 0           tf_op_layer_Shape_365[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_367 ( [()]                 0           tf_op_layer_Shape_367[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_369 ( [()]                 0           tf_op_layer_Shape_369[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_368 ( [()]                 0           tf_op_layer_Shape_368[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_216/shape ( [(3,)]               0           tf_op_layer_strided_slice_361[0][
                                                                 tf_op_layer_strided_slice_363[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_217/shape ( [(3,)]               0           tf_op_layer_strided_slice_362[0][
                                                                 tf_op_layer_strided_slice_363[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_218/shape ( [(3,)]               0           tf_op_layer_strided_slice_364[0][
                                                                 tf_op_layer_strided_slice_366[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_219/shape ( [(3,)]               0           tf_op_layer_strided_slice_365[0][
                                                                 tf_op_layer_strided_slice_366[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_220/shape ( [(3,)]               0           tf_op_layer_strided_slice_367[0][
                                                                 tf_op_layer_strided_slice_369[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_221/shape ( [(3,)]               0           tf_op_layer_strided_slice_368[0][
                                                                 tf_op_layer_strided_slice_369[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_216 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_36[0][0]         
                                                                 tf_op_layer_Reshape_216/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_216/multiples  [(3,)]               0           tf_op_layer_strided_slice_362[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_217 (Tensor [(1, None, None)]    0           tf_op_layer_Add_36[0][0]         
                                                                 tf_op_layer_Reshape_217/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_217/multiples  [(3,)]               0           tf_op_layer_strided_slice_361[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_218 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_218/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_218/multiples  [(3,)]               0           tf_op_layer_strided_slice_365[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_219 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_219/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_219/multiples  [(3,)]               0           tf_op_layer_strided_slice_364[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_220 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_36[0][0]         
                                                                 tf_op_layer_Reshape_220/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_220/multiples  [(3,)]               0           tf_op_layer_strided_slice_368[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_221 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_221/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_221/multiples  [(3,)]               0           tf_op_layer_strided_slice_367[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_216 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_216[0][0]    
                                                                 tf_op_layer_Tile_216/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_217 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_217[0][0]    
                                                                 tf_op_layer_Tile_217/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_218 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_218[0][0]    
                                                                 tf_op_layer_Tile_218/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_219 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_219[0][0]    
                                                                 tf_op_layer_Tile_219/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_220 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_220[0][0]    
                                                                 tf_op_layer_Tile_220/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_221 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_221[0][0]    
                                                                 tf_op_layer_Tile_221/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_180 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_216[0][0]       
                                                                 tf_op_layer_Tile_217[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_181 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_218[0][0]       
                                                                 tf_op_layer_Tile_219[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_182 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_220[0][0]       
                                                                 tf_op_layer_Tile_221[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_144 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_180[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_145 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_181[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_146 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_182[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_252 (TensorFlo [(None, None)]       0           tf_op_layer_Square_144[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_253 (TensorFlo [(None, None)]       0           tf_op_layer_Square_145[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_254 (TensorFlo [(None, None)]       0           tf_op_layer_Square_146[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_108 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_252[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_108 (TensorFlo [()]                 0           tf_op_layer_strided_slice_363[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_109 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_253[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_109 (TensorFlo [()]                 0           tf_op_layer_strided_slice_366[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_110 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_254[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_110 (TensorFlo [()]                 0           tf_op_layer_strided_slice_369[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_108 (Tensor [(None, None)]       0           tf_op_layer_Neg_108[0][0]        
                                                                 tf_op_layer_Cast_108[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_109 (Tensor [(None, None)]       0           tf_op_layer_Neg_109[0][0]        
                                                                 tf_op_layer_Cast_109[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_110 (Tensor [(None, None)]       0           tf_op_layer_Neg_110[0][0]        
                                                                 tf_op_layer_Cast_110[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_108 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_108[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_109 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_109[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_110 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_110[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_255 (TensorFlo [()]                 0           tf_op_layer_Exp_108[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_256 (TensorFlo [()]                 0           tf_op_layer_Exp_109[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_257 (TensorFlo [()]                 0           tf_op_layer_Exp_110[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_184 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_72 (TensorFlo [()]                 0           tf_op_layer_Mean_255[0][0]       
                                                                 tf_op_layer_Mean_256[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_109 (TensorFlow [()]                 0           tf_op_layer_Mean_257[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_147 (TensorF [(None, 97)]         0           tf_op_layer_Sub_184[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_183 (TensorFlow [()]                 0           tf_op_layer_AddV2_72[0][0]       
                                                                 tf_op_layer_Mul_109[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_36 (TensorFlowO [(None,)]            0           tf_op_layer_Square_147[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_110 (TensorFlow [()]                 0           tf_op_layer_Sub_183[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_73 (TensorFlo [(None,)]            0           tf_op_layer_Sum_36[0][0]         
                                                                 tf_op_layer_Mul_110[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_258 (TensorFlo [()]                 0           tf_op_layer_AddV2_73[0][0]       
__________________________________________________________________________________________________
add_loss_36 (AddLoss)           ()                   0           tf_op_layer_Mean_258[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.1882WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.1777s). Check your callbacks.
24/24 [==============================] - 1s 24ms/step - loss: 4.4463 - val_loss: 2.0202
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5826 - val_loss: 1.4129
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2949 - val_loss: 1.2282
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1857 - val_loss: 1.0628
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0179 - val_loss: 0.9103
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9226 - val_loss: 0.8812
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8927 - val_loss: 0.8845
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8655 - val_loss: 0.8586
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8455 - val_loss: 0.8610
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8296 - val_loss: 0.8265
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8206 - val_loss: 0.8439
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8258 - val_loss: 0.8342
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8110 - val_loss: 0.8331
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7975 - val_loss: 0.8200
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8034 - val_loss: 0.8400
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7878 - val_loss: 0.8576
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7980 - val_loss: 0.8257
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7892 - val_loss: 0.8328
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7957 - val_loss: 0.8110
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7756 - val_loss: 0.8093
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7746 - val_loss: 0.8190
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7619 - val_loss: 0.8361
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7592 - val_loss: 0.8169
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7589 - val_loss: 0.8176
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7576 - val_loss: 0.8219
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7564 - val_loss: 0.8130
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7605 - val_loss: 0.8231
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7450 - val_loss: 0.8349
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7488 - val_loss: 0.8225
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7388 - val_loss: 0.8276
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7366 - val_loss: 0.7844
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7375 - val_loss: 0.8254
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7478 - val_loss: 0.8221
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7279 - val_loss: 0.7963
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7299 - val_loss: 0.8149
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7214 - val_loss: 0.8023
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7206 - val_loss: 0.8053
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7141 - val_loss: 0.7939
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7323 - val_loss: 0.8190
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7251 - val_loss: 0.7902
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7194 - val_loss: 0.7783
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7107 - val_loss: 0.8074
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7177 - val_loss: 0.7837
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7101 - val_loss: 0.7913
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7027 - val_loss: 0.7938
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7105 - val_loss: 0.7798
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7051 - val_loss: 0.7976
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7104 - val_loss: 0.7774
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7050 - val_loss: 0.7819
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7036 - val_loss: 0.7999
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7037 - val_loss: 0.7905
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7020 - val_loss: 0.7657
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7039 - val_loss: 0.7784
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7030 - val_loss: 0.7884
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7087 - val_loss: 0.7874
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6968 - val_loss: 0.7723
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6961 - val_loss: 0.8128
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6975 - val_loss: 0.7533
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6913 - val_loss: 0.7700
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6979 - val_loss: 0.7532
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6923 - val_loss: 0.7792
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6817 - val_loss: 0.7599
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6864 - val_loss: 0.7596
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6859 - val_loss: 0.7925
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6902 - val_loss: 0.7628
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6895 - val_loss: 0.7715
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6881 - val_loss: 0.7655
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6867 - val_loss: 0.7472
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6710 - val_loss: 0.7590
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6834 - val_loss: 0.7685
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6845 - val_loss: 0.7661
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6862 - val_loss: 0.7593
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6869 - val_loss: 0.7569
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6802 - val_loss: 0.7443
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6743 - val_loss: 0.7474
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6727 - val_loss: 0.7642
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6822 - val_loss: 0.7519
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6799 - val_loss: 0.7867
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6736 - val_loss: 0.7502
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6830 - val_loss: 0.7801
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6720 - val_loss: 0.7680
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6866 - val_loss: 0.7728
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6740 - val_loss: 0.7679
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6683 - val_loss: 0.7559
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6739 - val_loss: 0.7879
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6693 - val_loss: 0.7569
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6721 - val_loss: 0.7647
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6710 - val_loss: 0.7599
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6683 - val_loss: 0.7716
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6732 - val_loss: 0.7550
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6797 - val_loss: 0.7505
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6716 - val_loss: 0.7533
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6608 - val_loss: 0.7553
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6725 - val_loss: 0.7828
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6730 - val_loss: 0.7410
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6733 - val_loss: 0.7455
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6662 - val_loss: 0.7526
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6711 - val_loss: 0.7518
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6645 - val_loss: 0.7470
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6742 - val_loss: 0.7540
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_113 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_370 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_370 ( [()]                 0           tf_op_layer_Shape_370[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_37 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_370[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_37[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_111 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_37 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_111[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_371 (TensorFl [(2,)]               0           tf_op_layer_Add_37[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_373 (TensorFl [(2,)]               0           tf_op_layer_Add_37[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_372 (TensorFl [(2,)]               0           tf_op_layer_Add_37[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_374 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_376 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_375 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_377 (TensorFl [(2,)]               0           tf_op_layer_Add_37[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_379 (TensorFl [(2,)]               0           tf_op_layer_Add_37[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_378 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_371 ( [()]                 0           tf_op_layer_Shape_371[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_373 ( [()]                 0           tf_op_layer_Shape_373[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_372 ( [()]                 0           tf_op_layer_Shape_372[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_374 ( [()]                 0           tf_op_layer_Shape_374[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_376 ( [()]                 0           tf_op_layer_Shape_376[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_375 ( [()]                 0           tf_op_layer_Shape_375[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_377 ( [()]                 0           tf_op_layer_Shape_377[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_379 ( [()]                 0           tf_op_layer_Shape_379[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_378 ( [()]                 0           tf_op_layer_Shape_378[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_222/shape ( [(3,)]               0           tf_op_layer_strided_slice_371[0][
                                                                 tf_op_layer_strided_slice_373[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_223/shape ( [(3,)]               0           tf_op_layer_strided_slice_372[0][
                                                                 tf_op_layer_strided_slice_373[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_224/shape ( [(3,)]               0           tf_op_layer_strided_slice_374[0][
                                                                 tf_op_layer_strided_slice_376[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_225/shape ( [(3,)]               0           tf_op_layer_strided_slice_375[0][
                                                                 tf_op_layer_strided_slice_376[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_226/shape ( [(3,)]               0           tf_op_layer_strided_slice_377[0][
                                                                 tf_op_layer_strided_slice_379[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_227/shape ( [(3,)]               0           tf_op_layer_strided_slice_378[0][
                                                                 tf_op_layer_strided_slice_379[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_222 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_37[0][0]         
                                                                 tf_op_layer_Reshape_222/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_222/multiples  [(3,)]               0           tf_op_layer_strided_slice_372[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_223 (Tensor [(1, None, None)]    0           tf_op_layer_Add_37[0][0]         
                                                                 tf_op_layer_Reshape_223/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_223/multiples  [(3,)]               0           tf_op_layer_strided_slice_371[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_224 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_224/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_224/multiples  [(3,)]               0           tf_op_layer_strided_slice_375[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_225 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_225/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_225/multiples  [(3,)]               0           tf_op_layer_strided_slice_374[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_226 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_37[0][0]         
                                                                 tf_op_layer_Reshape_226/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_226/multiples  [(3,)]               0           tf_op_layer_strided_slice_378[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_227 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_227/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_227/multiples  [(3,)]               0           tf_op_layer_strided_slice_377[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_222 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_222[0][0]    
                                                                 tf_op_layer_Tile_222/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_223 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_223[0][0]    
                                                                 tf_op_layer_Tile_223/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_224 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_224[0][0]    
                                                                 tf_op_layer_Tile_224/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_225 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_225[0][0]    
                                                                 tf_op_layer_Tile_225/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_226 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_226[0][0]    
                                                                 tf_op_layer_Tile_226/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_227 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_227[0][0]    
                                                                 tf_op_layer_Tile_227/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_185 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_222[0][0]       
                                                                 tf_op_layer_Tile_223[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_186 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_224[0][0]       
                                                                 tf_op_layer_Tile_225[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_187 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_226[0][0]       
                                                                 tf_op_layer_Tile_227[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_148 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_185[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_149 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_186[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_150 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_187[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_259 (TensorFlo [(None, None)]       0           tf_op_layer_Square_148[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_260 (TensorFlo [(None, None)]       0           tf_op_layer_Square_149[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_261 (TensorFlo [(None, None)]       0           tf_op_layer_Square_150[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_111 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_259[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_111 (TensorFlo [()]                 0           tf_op_layer_strided_slice_373[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_112 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_260[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_112 (TensorFlo [()]                 0           tf_op_layer_strided_slice_376[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_113 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_261[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_113 (TensorFlo [()]                 0           tf_op_layer_strided_slice_379[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_111 (Tensor [(None, None)]       0           tf_op_layer_Neg_111[0][0]        
                                                                 tf_op_layer_Cast_111[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_112 (Tensor [(None, None)]       0           tf_op_layer_Neg_112[0][0]        
                                                                 tf_op_layer_Cast_112[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_113 (Tensor [(None, None)]       0           tf_op_layer_Neg_113[0][0]        
                                                                 tf_op_layer_Cast_113[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_111 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_111[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_112 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_112[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_113 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_113[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_262 (TensorFlo [()]                 0           tf_op_layer_Exp_111[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_263 (TensorFlo [()]                 0           tf_op_layer_Exp_112[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_264 (TensorFlo [()]                 0           tf_op_layer_Exp_113[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_189 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_74 (TensorFlo [()]                 0           tf_op_layer_Mean_262[0][0]       
                                                                 tf_op_layer_Mean_263[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_112 (TensorFlow [()]                 0           tf_op_layer_Mean_264[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_151 (TensorF [(None, 97)]         0           tf_op_layer_Sub_189[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_188 (TensorFlow [()]                 0           tf_op_layer_AddV2_74[0][0]       
                                                                 tf_op_layer_Mul_112[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_37 (TensorFlowO [(None,)]            0           tf_op_layer_Square_151[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_113 (TensorFlow [()]                 0           tf_op_layer_Sub_188[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_75 (TensorFlo [(None,)]            0           tf_op_layer_Sum_37[0][0]         
                                                                 tf_op_layer_Mul_113[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_265 (TensorFlo [()]                 0           tf_op_layer_AddV2_75[0][0]       
__________________________________________________________________________________________________
add_loss_37 (AddLoss)           ()                   0           tf_op_layer_Mean_265[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.1378WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0013s vs `on_train_batch_end` time: 0.1257s). Check your callbacks.
24/24 [==============================] - 0s 19ms/step - loss: 8.4688 - val_loss: 6.1424
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.5076 - val_loss: 3.4718
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.6003 - val_loss: 2.2679
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.9464 - val_loss: 1.9040
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7519 - val_loss: 1.7398
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5815 - val_loss: 1.6131
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4562 - val_loss: 1.4805
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3771 - val_loss: 1.4405
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3196 - val_loss: 1.3280
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2998 - val_loss: 1.3526
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2447 - val_loss: 1.3228
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2120 - val_loss: 1.2477
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1766 - val_loss: 1.2818
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1786 - val_loss: 1.2481
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1908 - val_loss: 1.2651
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1517 - val_loss: 1.2079
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1383 - val_loss: 1.2325
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1348 - val_loss: 1.2095
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1362 - val_loss: 1.1472
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1078 - val_loss: 1.2076
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1223 - val_loss: 1.2101
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1057 - val_loss: 1.1882
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0955 - val_loss: 1.1716
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0798 - val_loss: 1.1274
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0852 - val_loss: 1.1575
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0731 - val_loss: 1.1337
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0656 - val_loss: 1.1330
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0582 - val_loss: 1.1302
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0665 - val_loss: 1.1272
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0554 - val_loss: 1.1243
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0825 - val_loss: 1.1221
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0481 - val_loss: 1.1297
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0621 - val_loss: 1.1527
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0532 - val_loss: 1.1306
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0456 - val_loss: 1.1135
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0513 - val_loss: 1.0937
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0343 - val_loss: 1.1335
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0375 - val_loss: 1.1145
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0480 - val_loss: 1.1195
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0467 - val_loss: 1.1279
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0396 - val_loss: 1.0886
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0319 - val_loss: 1.1055
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0345 - val_loss: 1.0907
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0285 - val_loss: 1.0889
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0319 - val_loss: 1.0843
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0118 - val_loss: 1.1081
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0299 - val_loss: 1.0958
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0463 - val_loss: 1.0836
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0118 - val_loss: 1.1026
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0185 - val_loss: 1.0799
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0271 - val_loss: 1.1168
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0173 - val_loss: 1.0781
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0187 - val_loss: 1.0776
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0139 - val_loss: 1.1033
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0277 - val_loss: 1.1141
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0250 - val_loss: 1.0936
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0057 - val_loss: 1.0886
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0057 - val_loss: 1.1205
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0352 - val_loss: 1.1132
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0020 - val_loss: 1.0865
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9975 - val_loss: 1.0994
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0109 - val_loss: 1.0587
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9967 - val_loss: 1.0830
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0032 - val_loss: 1.1075
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9959 - val_loss: 1.0854
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9905 - val_loss: 1.1081
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9913 - val_loss: 1.0598
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0069 - val_loss: 1.0772
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9855 - val_loss: 1.0844
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9884 - val_loss: 1.0846
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0020 - val_loss: 1.0854
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9994 - val_loss: 1.0649
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0193 - val_loss: 1.0858
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9809 - val_loss: 1.0753
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9834 - val_loss: 1.0857
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9824 - val_loss: 1.0866
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9799 - val_loss: 1.0626
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0032 - val_loss: 1.0491
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0012 - val_loss: 1.0586
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9884 - val_loss: 1.0419
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9972 - val_loss: 1.0624
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9925 - val_loss: 1.0850
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9814 - val_loss: 1.0816
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9783 - val_loss: 1.0386
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9801 - val_loss: 1.0587
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9839 - val_loss: 1.0476
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9790 - val_loss: 1.0587
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9898 - val_loss: 1.0568
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9827 - val_loss: 1.0475
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9737 - val_loss: 1.0563
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9807 - val_loss: 1.0567
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9748 - val_loss: 1.0390
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9855 - val_loss: 1.0462
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9746 - val_loss: 1.0448
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9735 - val_loss: 1.0214
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9726 - val_loss: 1.0642
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9746 - val_loss: 1.0473
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9744 - val_loss: 1.0587
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9912 - val_loss: 1.0384
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9844 - val_loss: 1.0545
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.30 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.33 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.68 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.25 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 7.45 seconds.
Calculated PHATE in 9.71 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_114 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_115 (Dense)               (None, 32)           2080        dense_114[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_115[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_115[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_116 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_117 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_118 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_114 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_115 (Dense)               (None, 32)           2080        dense_114[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_115[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_115[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_380 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_380 ( [()]                 0           tf_op_layer_Shape_380[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_38 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_380[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_38[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_114 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_38 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_114[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_381 (TensorFl [(2,)]               0           tf_op_layer_Add_38[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_383 (TensorFl [(2,)]               0           tf_op_layer_Add_38[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_382 (TensorFl [(2,)]               0           tf_op_layer_Add_38[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_384 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_386 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_385 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_387 (TensorFl [(2,)]               0           tf_op_layer_Add_38[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_389 (TensorFl [(2,)]               0           tf_op_layer_Add_38[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_388 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_381 ( [()]                 0           tf_op_layer_Shape_381[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_383 ( [()]                 0           tf_op_layer_Shape_383[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_382 ( [()]                 0           tf_op_layer_Shape_382[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_384 ( [()]                 0           tf_op_layer_Shape_384[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_386 ( [()]                 0           tf_op_layer_Shape_386[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_385 ( [()]                 0           tf_op_layer_Shape_385[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_387 ( [()]                 0           tf_op_layer_Shape_387[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_389 ( [()]                 0           tf_op_layer_Shape_389[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_388 ( [()]                 0           tf_op_layer_Shape_388[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_228/shape ( [(3,)]               0           tf_op_layer_strided_slice_381[0][
                                                                 tf_op_layer_strided_slice_383[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_229/shape ( [(3,)]               0           tf_op_layer_strided_slice_382[0][
                                                                 tf_op_layer_strided_slice_383[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_230/shape ( [(3,)]               0           tf_op_layer_strided_slice_384[0][
                                                                 tf_op_layer_strided_slice_386[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_231/shape ( [(3,)]               0           tf_op_layer_strided_slice_385[0][
                                                                 tf_op_layer_strided_slice_386[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_232/shape ( [(3,)]               0           tf_op_layer_strided_slice_387[0][
                                                                 tf_op_layer_strided_slice_389[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_233/shape ( [(3,)]               0           tf_op_layer_strided_slice_388[0][
                                                                 tf_op_layer_strided_slice_389[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_228 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_38[0][0]         
                                                                 tf_op_layer_Reshape_228/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_228/multiples  [(3,)]               0           tf_op_layer_strided_slice_382[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_229 (Tensor [(1, None, None)]    0           tf_op_layer_Add_38[0][0]         
                                                                 tf_op_layer_Reshape_229/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_229/multiples  [(3,)]               0           tf_op_layer_strided_slice_381[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_230 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_230/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_230/multiples  [(3,)]               0           tf_op_layer_strided_slice_385[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_231 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_231/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_231/multiples  [(3,)]               0           tf_op_layer_strided_slice_384[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_232 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_38[0][0]         
                                                                 tf_op_layer_Reshape_232/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_232/multiples  [(3,)]               0           tf_op_layer_strided_slice_388[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_233 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_233/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_233/multiples  [(3,)]               0           tf_op_layer_strided_slice_387[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_228 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_228[0][0]    
                                                                 tf_op_layer_Tile_228/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_229 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_229[0][0]    
                                                                 tf_op_layer_Tile_229/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_230 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_230[0][0]    
                                                                 tf_op_layer_Tile_230/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_231 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_231[0][0]    
                                                                 tf_op_layer_Tile_231/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_232 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_232[0][0]    
                                                                 tf_op_layer_Tile_232/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_233 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_233[0][0]    
                                                                 tf_op_layer_Tile_233/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_190 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_228[0][0]       
                                                                 tf_op_layer_Tile_229[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_191 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_230[0][0]       
                                                                 tf_op_layer_Tile_231[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_192 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_232[0][0]       
                                                                 tf_op_layer_Tile_233[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_152 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_190[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_153 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_191[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_154 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_192[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_266 (TensorFlo [(None, None)]       0           tf_op_layer_Square_152[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_267 (TensorFlo [(None, None)]       0           tf_op_layer_Square_153[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_268 (TensorFlo [(None, None)]       0           tf_op_layer_Square_154[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_114 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_266[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_114 (TensorFlo [()]                 0           tf_op_layer_strided_slice_383[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_115 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_267[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_115 (TensorFlo [()]                 0           tf_op_layer_strided_slice_386[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_116 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_268[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_116 (TensorFlo [()]                 0           tf_op_layer_strided_slice_389[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_114 (Tensor [(None, None)]       0           tf_op_layer_Neg_114[0][0]        
                                                                 tf_op_layer_Cast_114[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_115 (Tensor [(None, None)]       0           tf_op_layer_Neg_115[0][0]        
                                                                 tf_op_layer_Cast_115[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_116 (Tensor [(None, None)]       0           tf_op_layer_Neg_116[0][0]        
                                                                 tf_op_layer_Cast_116[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_114 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_114[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_115 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_115[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_116 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_116[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_269 (TensorFlo [()]                 0           tf_op_layer_Exp_114[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_270 (TensorFlo [()]                 0           tf_op_layer_Exp_115[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_271 (TensorFlo [()]                 0           tf_op_layer_Exp_116[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_194 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_76 (TensorFlo [()]                 0           tf_op_layer_Mean_269[0][0]       
                                                                 tf_op_layer_Mean_270[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_115 (TensorFlow [()]                 0           tf_op_layer_Mean_271[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_155 (TensorF [(None, 97)]         0           tf_op_layer_Sub_194[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_193 (TensorFlow [()]                 0           tf_op_layer_AddV2_76[0][0]       
                                                                 tf_op_layer_Mul_115[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_38 (TensorFlowO [(None,)]            0           tf_op_layer_Square_155[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_116 (TensorFlow [()]                 0           tf_op_layer_Sub_193[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_77 (TensorFlo [(None,)]            0           tf_op_layer_Sum_38[0][0]         
                                                                 tf_op_layer_Mul_116[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_272 (TensorFlo [()]                 0           tf_op_layer_AddV2_77[0][0]       
__________________________________________________________________________________________________
add_loss_38 (AddLoss)           ()                   0           tf_op_layer_Mean_272[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.1534WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.1412s). Check your callbacks.
24/24 [==============================] - 1s 21ms/step - loss: 5.6858 - val_loss: 3.8765
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 3.0308 - val_loss: 1.8238
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5432 - val_loss: 1.2486
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2749 - val_loss: 1.1761
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2041 - val_loss: 1.0936
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1593 - val_loss: 1.1142
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1241 - val_loss: 1.1348
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1292 - val_loss: 1.0746
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1077 - val_loss: 1.0547
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0880 - val_loss: 1.0362
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0715 - val_loss: 0.9571
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0209 - val_loss: 0.9646
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9739 - val_loss: 0.8873
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9245 - val_loss: 0.8515
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8823 - val_loss: 0.7912
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8564 - val_loss: 0.8150
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8543 - val_loss: 0.7895
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8268 - val_loss: 0.7500
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8288 - val_loss: 0.7661
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8244 - val_loss: 0.7995
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8292 - val_loss: 0.7769
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8177 - val_loss: 0.7456
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8147 - val_loss: 0.7610
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8143 - val_loss: 0.7554
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8010 - val_loss: 0.7239
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7954 - val_loss: 0.7686
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7823 - val_loss: 0.7281
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8052 - val_loss: 0.7170
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7918 - val_loss: 0.7253
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7861 - val_loss: 0.7650
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8010 - val_loss: 0.7145
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7747 - val_loss: 0.7145
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7794 - val_loss: 0.7617
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7789 - val_loss: 0.7083
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7810 - val_loss: 0.7305
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7768 - val_loss: 0.7552
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7735 - val_loss: 0.7409
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7708 - val_loss: 0.7285
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7737 - val_loss: 0.7222
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7585 - val_loss: 0.7081
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7639 - val_loss: 0.7230
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7632 - val_loss: 0.7052
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7646 - val_loss: 0.7299
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7627 - val_loss: 0.7272
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7655 - val_loss: 0.6910
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7590 - val_loss: 0.7268
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7593 - val_loss: 0.6995
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7579 - val_loss: 0.6835
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7499 - val_loss: 0.6963
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7517 - val_loss: 0.6788
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7468 - val_loss: 0.6896
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7560 - val_loss: 0.7376
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7419 - val_loss: 0.6905
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7451 - val_loss: 0.7065
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7373 - val_loss: 0.7049
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7399 - val_loss: 0.6764
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7460 - val_loss: 0.7031
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7471 - val_loss: 0.7634
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7493 - val_loss: 0.7224
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7410 - val_loss: 0.6952
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7362 - val_loss: 0.6940
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7473 - val_loss: 0.6860
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7460 - val_loss: 0.7092
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7398 - val_loss: 0.6903
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7294 - val_loss: 0.6849
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7309 - val_loss: 0.6792
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7399 - val_loss: 0.7085
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7285 - val_loss: 0.7047
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7349 - val_loss: 0.7051
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7282 - val_loss: 0.6862
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7327 - val_loss: 0.6873
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7246 - val_loss: 0.7063
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7272 - val_loss: 0.6877
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7355 - val_loss: 0.7433
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7325 - val_loss: 0.7207
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7359 - val_loss: 0.7096
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7184 - val_loss: 0.6900
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7284 - val_loss: 0.6735
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7327 - val_loss: 0.7141
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7261 - val_loss: 0.7047
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7282 - val_loss: 0.6801
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7228 - val_loss: 0.6797
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7343 - val_loss: 0.6916
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7186 - val_loss: 0.6799
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7179 - val_loss: 0.6647
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7134 - val_loss: 0.7164
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7152 - val_loss: 0.6823
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7205 - val_loss: 0.6777
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7200 - val_loss: 0.7004
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7175 - val_loss: 0.6667
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7113 - val_loss: 0.6978
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7163 - val_loss: 0.6893
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7202 - val_loss: 0.6964
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7095 - val_loss: 0.6915
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7067 - val_loss: 0.7032
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7184 - val_loss: 0.7148
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7189 - val_loss: 0.7148
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7169 - val_loss: 0.7075
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7161 - val_loss: 0.6761
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7199 - val_loss: 0.6699
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_119 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_390 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_390 ( [()]                 0           tf_op_layer_Shape_390[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_39 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_390[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_39[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_117 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_39 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_117[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_391 (TensorFl [(2,)]               0           tf_op_layer_Add_39[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_393 (TensorFl [(2,)]               0           tf_op_layer_Add_39[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_392 (TensorFl [(2,)]               0           tf_op_layer_Add_39[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_394 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_396 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_395 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_397 (TensorFl [(2,)]               0           tf_op_layer_Add_39[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_399 (TensorFl [(2,)]               0           tf_op_layer_Add_39[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_398 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_391 ( [()]                 0           tf_op_layer_Shape_391[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_393 ( [()]                 0           tf_op_layer_Shape_393[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_392 ( [()]                 0           tf_op_layer_Shape_392[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_394 ( [()]                 0           tf_op_layer_Shape_394[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_396 ( [()]                 0           tf_op_layer_Shape_396[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_395 ( [()]                 0           tf_op_layer_Shape_395[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_397 ( [()]                 0           tf_op_layer_Shape_397[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_399 ( [()]                 0           tf_op_layer_Shape_399[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_398 ( [()]                 0           tf_op_layer_Shape_398[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_234/shape ( [(3,)]               0           tf_op_layer_strided_slice_391[0][
                                                                 tf_op_layer_strided_slice_393[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_235/shape ( [(3,)]               0           tf_op_layer_strided_slice_392[0][
                                                                 tf_op_layer_strided_slice_393[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_236/shape ( [(3,)]               0           tf_op_layer_strided_slice_394[0][
                                                                 tf_op_layer_strided_slice_396[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_237/shape ( [(3,)]               0           tf_op_layer_strided_slice_395[0][
                                                                 tf_op_layer_strided_slice_396[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_238/shape ( [(3,)]               0           tf_op_layer_strided_slice_397[0][
                                                                 tf_op_layer_strided_slice_399[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_239/shape ( [(3,)]               0           tf_op_layer_strided_slice_398[0][
                                                                 tf_op_layer_strided_slice_399[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_234 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_39[0][0]         
                                                                 tf_op_layer_Reshape_234/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_234/multiples  [(3,)]               0           tf_op_layer_strided_slice_392[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_235 (Tensor [(1, None, None)]    0           tf_op_layer_Add_39[0][0]         
                                                                 tf_op_layer_Reshape_235/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_235/multiples  [(3,)]               0           tf_op_layer_strided_slice_391[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_236 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_236/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_236/multiples  [(3,)]               0           tf_op_layer_strided_slice_395[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_237 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_237/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_237/multiples  [(3,)]               0           tf_op_layer_strided_slice_394[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_238 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_39[0][0]         
                                                                 tf_op_layer_Reshape_238/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_238/multiples  [(3,)]               0           tf_op_layer_strided_slice_398[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_239 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_239/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_239/multiples  [(3,)]               0           tf_op_layer_strided_slice_397[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_234 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_234[0][0]    
                                                                 tf_op_layer_Tile_234/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_235 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_235[0][0]    
                                                                 tf_op_layer_Tile_235/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_236 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_236[0][0]    
                                                                 tf_op_layer_Tile_236/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_237 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_237[0][0]    
                                                                 tf_op_layer_Tile_237/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_238 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_238[0][0]    
                                                                 tf_op_layer_Tile_238/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_239 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_239[0][0]    
                                                                 tf_op_layer_Tile_239/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_195 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_234[0][0]       
                                                                 tf_op_layer_Tile_235[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_196 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_236[0][0]       
                                                                 tf_op_layer_Tile_237[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_197 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_238[0][0]       
                                                                 tf_op_layer_Tile_239[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_156 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_195[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_157 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_196[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_158 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_197[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_273 (TensorFlo [(None, None)]       0           tf_op_layer_Square_156[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_274 (TensorFlo [(None, None)]       0           tf_op_layer_Square_157[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_275 (TensorFlo [(None, None)]       0           tf_op_layer_Square_158[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_117 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_273[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_117 (TensorFlo [()]                 0           tf_op_layer_strided_slice_393[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_118 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_274[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_118 (TensorFlo [()]                 0           tf_op_layer_strided_slice_396[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_119 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_275[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_119 (TensorFlo [()]                 0           tf_op_layer_strided_slice_399[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_117 (Tensor [(None, None)]       0           tf_op_layer_Neg_117[0][0]        
                                                                 tf_op_layer_Cast_117[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_118 (Tensor [(None, None)]       0           tf_op_layer_Neg_118[0][0]        
                                                                 tf_op_layer_Cast_118[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_119 (Tensor [(None, None)]       0           tf_op_layer_Neg_119[0][0]        
                                                                 tf_op_layer_Cast_119[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_117 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_117[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_118 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_118[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_119 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_119[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_276 (TensorFlo [()]                 0           tf_op_layer_Exp_117[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_277 (TensorFlo [()]                 0           tf_op_layer_Exp_118[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_278 (TensorFlo [()]                 0           tf_op_layer_Exp_119[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_199 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_78 (TensorFlo [()]                 0           tf_op_layer_Mean_276[0][0]       
                                                                 tf_op_layer_Mean_277[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_118 (TensorFlow [()]                 0           tf_op_layer_Mean_278[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_159 (TensorF [(None, 97)]         0           tf_op_layer_Sub_199[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_198 (TensorFlow [()]                 0           tf_op_layer_AddV2_78[0][0]       
                                                                 tf_op_layer_Mul_118[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_39 (TensorFlowO [(None,)]            0           tf_op_layer_Square_159[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_119 (TensorFlow [()]                 0           tf_op_layer_Sub_198[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_79 (TensorFlo [(None,)]            0           tf_op_layer_Sum_39[0][0]         
                                                                 tf_op_layer_Mul_119[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_279 (TensorFlo [()]                 0           tf_op_layer_AddV2_79[0][0]       
__________________________________________________________________________________________________
add_loss_39 (AddLoss)           ()                   0           tf_op_layer_Mean_279[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.9594 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.1775s). Check your callbacks.
24/24 [==============================] - 0s 20ms/step - loss: 7.8286 - val_loss: 4.8722
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.3732 - val_loss: 3.0316
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.8679 - val_loss: 2.1649
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.1890 - val_loss: 1.8494
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.9250 - val_loss: 1.7087
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7998 - val_loss: 1.6009
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6931 - val_loss: 1.5528
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6208 - val_loss: 1.5343
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5749 - val_loss: 1.4427
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5267 - val_loss: 1.3735
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4880 - val_loss: 1.3673
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4701 - val_loss: 1.2973
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3991 - val_loss: 1.2488
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3601 - val_loss: 1.2207
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3016 - val_loss: 1.1822
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2760 - val_loss: 1.1487
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2519 - val_loss: 1.1305
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2281 - val_loss: 1.0704
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2081 - val_loss: 1.0902
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1835 - val_loss: 1.0528
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1781 - val_loss: 1.0387
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1664 - val_loss: 1.0521
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1429 - val_loss: 1.0247
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1407 - val_loss: 1.0392
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1266 - val_loss: 1.0369
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1185 - val_loss: 1.0583
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1202 - val_loss: 0.9976
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1206 - val_loss: 0.9969
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1009 - val_loss: 1.0147
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1136 - val_loss: 0.9853
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0870 - val_loss: 0.9912
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0917 - val_loss: 0.9739
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0897 - val_loss: 0.9971
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0747 - val_loss: 0.9704
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0719 - val_loss: 0.9628
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0878 - val_loss: 0.9692
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0705 - val_loss: 0.9659
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0778 - val_loss: 0.9721
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0694 - val_loss: 0.9419
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0554 - val_loss: 0.9440
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0667 - val_loss: 0.9358
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0403 - val_loss: 0.9553
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0546 - val_loss: 0.9635
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0579 - val_loss: 0.9234
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0360 - val_loss: 0.9229
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0587 - val_loss: 0.9280
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0509 - val_loss: 0.9143
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0509 - val_loss: 0.9344
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0312 - val_loss: 0.9602
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0321 - val_loss: 0.9486
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0467 - val_loss: 0.9174
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0355 - val_loss: 0.9046
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0266 - val_loss: 0.9207
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0420 - val_loss: 0.9481
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0304 - val_loss: 0.9193
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0374 - val_loss: 0.9405
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0279 - val_loss: 0.9234
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0214 - val_loss: 0.9349
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0378 - val_loss: 0.9306
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0247 - val_loss: 0.9024
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0235 - val_loss: 0.9403
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0176 - val_loss: 0.9078
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0136 - val_loss: 0.8979
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0212 - val_loss: 0.9313
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0218 - val_loss: 0.8985
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0082 - val_loss: 0.8792
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0204 - val_loss: 0.9066
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0197 - val_loss: 0.9119
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0189 - val_loss: 0.8968
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0250 - val_loss: 0.9053
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0212 - val_loss: 0.9128
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0091 - val_loss: 0.8881
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0074 - val_loss: 0.9107
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0045 - val_loss: 0.8691
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0168 - val_loss: 0.8939
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0103 - val_loss: 0.9125
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0085 - val_loss: 0.8900
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0048 - val_loss: 0.8932
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0106 - val_loss: 0.8845
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0051 - val_loss: 0.9013
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0084 - val_loss: 0.8762
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9949 - val_loss: 0.8651
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0100 - val_loss: 0.8893
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0077 - val_loss: 0.8805
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9939 - val_loss: 0.8749
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0003 - val_loss: 0.8945
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0074 - val_loss: 0.9021
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9926 - val_loss: 0.8777
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0032 - val_loss: 0.8970
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0033 - val_loss: 0.8871
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0055 - val_loss: 0.8837
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9926 - val_loss: 0.8803
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0139 - val_loss: 0.8763
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0043 - val_loss: 0.8833
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9929 - val_loss: 0.8904
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9925 - val_loss: 0.8804
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9875 - val_loss: 0.8938
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9995 - val_loss: 0.8927
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9930 - val_loss: 0.8719
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9986 - val_loss: 0.8679
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.29 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.31 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.42 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.23 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 7.24 seconds.
Calculated PHATE in 9.22 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_120 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_121 (Dense)               (None, 32)           2080        dense_120[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_121[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_121[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_122 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_123 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_124 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_120 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_121 (Dense)               (None, 32)           2080        dense_120[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_121[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_121[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_400 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_400 ( [()]                 0           tf_op_layer_Shape_400[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_40 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_400[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_40[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_120 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_40 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_120[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_401 (TensorFl [(2,)]               0           tf_op_layer_Add_40[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_403 (TensorFl [(2,)]               0           tf_op_layer_Add_40[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_402 (TensorFl [(2,)]               0           tf_op_layer_Add_40[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_404 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_406 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_405 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_407 (TensorFl [(2,)]               0           tf_op_layer_Add_40[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_409 (TensorFl [(2,)]               0           tf_op_layer_Add_40[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_408 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_401 ( [()]                 0           tf_op_layer_Shape_401[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_403 ( [()]                 0           tf_op_layer_Shape_403[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_402 ( [()]                 0           tf_op_layer_Shape_402[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_404 ( [()]                 0           tf_op_layer_Shape_404[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_406 ( [()]                 0           tf_op_layer_Shape_406[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_405 ( [()]                 0           tf_op_layer_Shape_405[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_407 ( [()]                 0           tf_op_layer_Shape_407[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_409 ( [()]                 0           tf_op_layer_Shape_409[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_408 ( [()]                 0           tf_op_layer_Shape_408[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_240/shape ( [(3,)]               0           tf_op_layer_strided_slice_401[0][
                                                                 tf_op_layer_strided_slice_403[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_241/shape ( [(3,)]               0           tf_op_layer_strided_slice_402[0][
                                                                 tf_op_layer_strided_slice_403[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_242/shape ( [(3,)]               0           tf_op_layer_strided_slice_404[0][
                                                                 tf_op_layer_strided_slice_406[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_243/shape ( [(3,)]               0           tf_op_layer_strided_slice_405[0][
                                                                 tf_op_layer_strided_slice_406[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_244/shape ( [(3,)]               0           tf_op_layer_strided_slice_407[0][
                                                                 tf_op_layer_strided_slice_409[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_245/shape ( [(3,)]               0           tf_op_layer_strided_slice_408[0][
                                                                 tf_op_layer_strided_slice_409[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_240 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_40[0][0]         
                                                                 tf_op_layer_Reshape_240/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_240/multiples  [(3,)]               0           tf_op_layer_strided_slice_402[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_241 (Tensor [(1, None, None)]    0           tf_op_layer_Add_40[0][0]         
                                                                 tf_op_layer_Reshape_241/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_241/multiples  [(3,)]               0           tf_op_layer_strided_slice_401[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_242 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_242/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_242/multiples  [(3,)]               0           tf_op_layer_strided_slice_405[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_243 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_243/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_243/multiples  [(3,)]               0           tf_op_layer_strided_slice_404[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_244 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_40[0][0]         
                                                                 tf_op_layer_Reshape_244/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_244/multiples  [(3,)]               0           tf_op_layer_strided_slice_408[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_245 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_245/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_245/multiples  [(3,)]               0           tf_op_layer_strided_slice_407[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_240 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_240[0][0]    
                                                                 tf_op_layer_Tile_240/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_241 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_241[0][0]    
                                                                 tf_op_layer_Tile_241/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_242 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_242[0][0]    
                                                                 tf_op_layer_Tile_242/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_243 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_243[0][0]    
                                                                 tf_op_layer_Tile_243/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_244 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_244[0][0]    
                                                                 tf_op_layer_Tile_244/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_245 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_245[0][0]    
                                                                 tf_op_layer_Tile_245/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_200 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_240[0][0]       
                                                                 tf_op_layer_Tile_241[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_201 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_242[0][0]       
                                                                 tf_op_layer_Tile_243[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_202 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_244[0][0]       
                                                                 tf_op_layer_Tile_245[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_160 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_200[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_161 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_201[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_162 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_202[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_280 (TensorFlo [(None, None)]       0           tf_op_layer_Square_160[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_281 (TensorFlo [(None, None)]       0           tf_op_layer_Square_161[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_282 (TensorFlo [(None, None)]       0           tf_op_layer_Square_162[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_120 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_280[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_120 (TensorFlo [()]                 0           tf_op_layer_strided_slice_403[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_121 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_281[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_121 (TensorFlo [()]                 0           tf_op_layer_strided_slice_406[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_122 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_282[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_122 (TensorFlo [()]                 0           tf_op_layer_strided_slice_409[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_120 (Tensor [(None, None)]       0           tf_op_layer_Neg_120[0][0]        
                                                                 tf_op_layer_Cast_120[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_121 (Tensor [(None, None)]       0           tf_op_layer_Neg_121[0][0]        
                                                                 tf_op_layer_Cast_121[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_122 (Tensor [(None, None)]       0           tf_op_layer_Neg_122[0][0]        
                                                                 tf_op_layer_Cast_122[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_120 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_120[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_121 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_121[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_122 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_122[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_283 (TensorFlo [()]                 0           tf_op_layer_Exp_120[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_284 (TensorFlo [()]                 0           tf_op_layer_Exp_121[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_285 (TensorFlo [()]                 0           tf_op_layer_Exp_122[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_204 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_80 (TensorFlo [()]                 0           tf_op_layer_Mean_283[0][0]       
                                                                 tf_op_layer_Mean_284[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_121 (TensorFlow [()]                 0           tf_op_layer_Mean_285[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_163 (TensorF [(None, 97)]         0           tf_op_layer_Sub_204[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_203 (TensorFlow [()]                 0           tf_op_layer_AddV2_80[0][0]       
                                                                 tf_op_layer_Mul_121[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_40 (TensorFlowO [(None,)]            0           tf_op_layer_Square_163[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_122 (TensorFlow [()]                 0           tf_op_layer_Sub_203[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_81 (TensorFlo [(None,)]            0           tf_op_layer_Sum_40[0][0]         
                                                                 tf_op_layer_Mul_122[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_286 (TensorFlo [()]                 0           tf_op_layer_AddV2_81[0][0]       
__________________________________________________________________________________________________
add_loss_40 (AddLoss)           ()                   0           tf_op_layer_Mean_286[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.0650WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.2128s). Check your callbacks.
24/24 [==============================] - 1s 27ms/step - loss: 5.0181 - val_loss: 2.9033
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8209 - val_loss: 1.7769
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3135 - val_loss: 1.4926
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2049 - val_loss: 1.4293
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1423 - val_loss: 1.3484
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0560 - val_loss: 1.2397
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9861 - val_loss: 1.1150
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9043 - val_loss: 1.0815
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8807 - val_loss: 1.0131
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8497 - val_loss: 0.9606
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8247 - val_loss: 0.9698
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8131 - val_loss: 0.9574
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7916 - val_loss: 0.9472
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7958 - val_loss: 0.9598
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7909 - val_loss: 0.9464
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7972 - val_loss: 0.9208
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7848 - val_loss: 0.9219
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7793 - val_loss: 0.9048
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7795 - val_loss: 0.8814
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7727 - val_loss: 0.8999
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7682 - val_loss: 0.8946
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7735 - val_loss: 0.8642
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7643 - val_loss: 0.8738
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7570 - val_loss: 0.8959
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7594 - val_loss: 0.8607
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7716 - val_loss: 0.8795
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7491 - val_loss: 0.9164
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7505 - val_loss: 0.8833
Epoch 29/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7473 - val_loss: 0.9058
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7482 - val_loss: 0.8538
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7466 - val_loss: 0.8475
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7388 - val_loss: 0.8585
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7472 - val_loss: 0.8518
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7422 - val_loss: 0.8643
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7465 - val_loss: 0.8491
Epoch 36/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7370 - val_loss: 0.8439
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7366 - val_loss: 0.8648
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7490 - val_loss: 0.8613
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7375 - val_loss: 0.8639
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7358 - val_loss: 0.8804
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7473 - val_loss: 0.8605
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7299 - val_loss: 0.8497
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7314 - val_loss: 0.8308
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7282 - val_loss: 0.8205
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7324 - val_loss: 0.8416
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7271 - val_loss: 0.8842
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7213 - val_loss: 0.8478
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7276 - val_loss: 0.8661
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7271 - val_loss: 0.8496
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7186 - val_loss: 0.8429
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7295 - val_loss: 0.8251
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7284 - val_loss: 0.8455
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7191 - val_loss: 0.8245
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7258 - val_loss: 0.8219
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7206 - val_loss: 0.8383
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7186 - val_loss: 0.8268
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7235 - val_loss: 0.8507
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7205 - val_loss: 0.8296
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7131 - val_loss: 0.8546
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7145 - val_loss: 0.8730
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7112 - val_loss: 0.8388
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7179 - val_loss: 0.8546
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7190 - val_loss: 0.8295
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7146 - val_loss: 0.8072
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7084 - val_loss: 0.8820
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7230 - val_loss: 0.8221
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7262 - val_loss: 0.8623
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7074 - val_loss: 0.8340
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7151 - val_loss: 0.8487
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7181 - val_loss: 0.8399
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7142 - val_loss: 0.8254
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7011 - val_loss: 0.8247
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7066 - val_loss: 0.8230
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7000 - val_loss: 0.8609
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7081 - val_loss: 0.8497
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7103 - val_loss: 0.8103
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6960 - val_loss: 0.8230
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7032 - val_loss: 0.8443
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7042 - val_loss: 0.8282
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7110 - val_loss: 0.8397
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7022 - val_loss: 0.8099
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7002 - val_loss: 0.8636
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7076 - val_loss: 0.8189
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7058 - val_loss: 0.8791
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7053 - val_loss: 0.8489
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7059 - val_loss: 0.8369
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7006 - val_loss: 0.8541
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6972 - val_loss: 0.8366
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6919 - val_loss: 0.8452
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7038 - val_loss: 0.8164
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7047 - val_loss: 0.8160
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6927 - val_loss: 0.8233
Epoch 93/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6956 - val_loss: 0.7938
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7067 - val_loss: 0.8360
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7024 - val_loss: 0.8791
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7001 - val_loss: 0.8298
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6864 - val_loss: 0.8141
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6915 - val_loss: 0.8228
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6889 - val_loss: 0.8221
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6917 - val_loss: 0.8563
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_125 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_410 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_410 ( [()]                 0           tf_op_layer_Shape_410[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_41 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_410[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_41[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_123 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_41 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_123[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_411 (TensorFl [(2,)]               0           tf_op_layer_Add_41[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_413 (TensorFl [(2,)]               0           tf_op_layer_Add_41[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_412 (TensorFl [(2,)]               0           tf_op_layer_Add_41[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_414 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_416 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_415 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_417 (TensorFl [(2,)]               0           tf_op_layer_Add_41[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_419 (TensorFl [(2,)]               0           tf_op_layer_Add_41[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_418 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_411 ( [()]                 0           tf_op_layer_Shape_411[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_413 ( [()]                 0           tf_op_layer_Shape_413[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_412 ( [()]                 0           tf_op_layer_Shape_412[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_414 ( [()]                 0           tf_op_layer_Shape_414[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_416 ( [()]                 0           tf_op_layer_Shape_416[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_415 ( [()]                 0           tf_op_layer_Shape_415[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_417 ( [()]                 0           tf_op_layer_Shape_417[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_419 ( [()]                 0           tf_op_layer_Shape_419[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_418 ( [()]                 0           tf_op_layer_Shape_418[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_246/shape ( [(3,)]               0           tf_op_layer_strided_slice_411[0][
                                                                 tf_op_layer_strided_slice_413[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_247/shape ( [(3,)]               0           tf_op_layer_strided_slice_412[0][
                                                                 tf_op_layer_strided_slice_413[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_248/shape ( [(3,)]               0           tf_op_layer_strided_slice_414[0][
                                                                 tf_op_layer_strided_slice_416[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_249/shape ( [(3,)]               0           tf_op_layer_strided_slice_415[0][
                                                                 tf_op_layer_strided_slice_416[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_250/shape ( [(3,)]               0           tf_op_layer_strided_slice_417[0][
                                                                 tf_op_layer_strided_slice_419[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_251/shape ( [(3,)]               0           tf_op_layer_strided_slice_418[0][
                                                                 tf_op_layer_strided_slice_419[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_246 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_41[0][0]         
                                                                 tf_op_layer_Reshape_246/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_246/multiples  [(3,)]               0           tf_op_layer_strided_slice_412[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_247 (Tensor [(1, None, None)]    0           tf_op_layer_Add_41[0][0]         
                                                                 tf_op_layer_Reshape_247/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_247/multiples  [(3,)]               0           tf_op_layer_strided_slice_411[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_248 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_248/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_248/multiples  [(3,)]               0           tf_op_layer_strided_slice_415[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_249 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_249/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_249/multiples  [(3,)]               0           tf_op_layer_strided_slice_414[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_250 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_41[0][0]         
                                                                 tf_op_layer_Reshape_250/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_250/multiples  [(3,)]               0           tf_op_layer_strided_slice_418[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_251 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_251/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_251/multiples  [(3,)]               0           tf_op_layer_strided_slice_417[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_246 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_246[0][0]    
                                                                 tf_op_layer_Tile_246/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_247 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_247[0][0]    
                                                                 tf_op_layer_Tile_247/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_248 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_248[0][0]    
                                                                 tf_op_layer_Tile_248/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_249 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_249[0][0]    
                                                                 tf_op_layer_Tile_249/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_250 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_250[0][0]    
                                                                 tf_op_layer_Tile_250/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_251 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_251[0][0]    
                                                                 tf_op_layer_Tile_251/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_205 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_246[0][0]       
                                                                 tf_op_layer_Tile_247[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_206 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_248[0][0]       
                                                                 tf_op_layer_Tile_249[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_207 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_250[0][0]       
                                                                 tf_op_layer_Tile_251[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_164 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_205[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_165 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_206[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_166 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_207[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_287 (TensorFlo [(None, None)]       0           tf_op_layer_Square_164[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_288 (TensorFlo [(None, None)]       0           tf_op_layer_Square_165[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_289 (TensorFlo [(None, None)]       0           tf_op_layer_Square_166[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_123 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_287[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_123 (TensorFlo [()]                 0           tf_op_layer_strided_slice_413[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_124 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_288[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_124 (TensorFlo [()]                 0           tf_op_layer_strided_slice_416[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_125 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_289[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_125 (TensorFlo [()]                 0           tf_op_layer_strided_slice_419[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_123 (Tensor [(None, None)]       0           tf_op_layer_Neg_123[0][0]        
                                                                 tf_op_layer_Cast_123[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_124 (Tensor [(None, None)]       0           tf_op_layer_Neg_124[0][0]        
                                                                 tf_op_layer_Cast_124[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_125 (Tensor [(None, None)]       0           tf_op_layer_Neg_125[0][0]        
                                                                 tf_op_layer_Cast_125[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_123 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_123[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_124 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_124[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_125 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_125[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_290 (TensorFlo [()]                 0           tf_op_layer_Exp_123[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_291 (TensorFlo [()]                 0           tf_op_layer_Exp_124[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_292 (TensorFlo [()]                 0           tf_op_layer_Exp_125[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_209 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_82 (TensorFlo [()]                 0           tf_op_layer_Mean_290[0][0]       
                                                                 tf_op_layer_Mean_291[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_124 (TensorFlow [()]                 0           tf_op_layer_Mean_292[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_167 (TensorF [(None, 97)]         0           tf_op_layer_Sub_209[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_208 (TensorFlow [()]                 0           tf_op_layer_AddV2_82[0][0]       
                                                                 tf_op_layer_Mul_124[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_41 (TensorFlowO [(None,)]            0           tf_op_layer_Square_167[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_125 (TensorFlow [()]                 0           tf_op_layer_Sub_208[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_83 (TensorFlo [(None,)]            0           tf_op_layer_Sum_41[0][0]         
                                                                 tf_op_layer_Mul_125[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_293 (TensorFlo [()]                 0           tf_op_layer_AddV2_83[0][0]       
__________________________________________________________________________________________________
add_loss_41 (AddLoss)           ()                   0           tf_op_layer_Mean_293[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.5331WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.2113s). Check your callbacks.
24/24 [==============================] - 1s 26ms/step - loss: 8.1488 - val_loss: 5.6712
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 4.0392 - val_loss: 2.7438
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 2.3622 - val_loss: 2.0022
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.9164 - val_loss: 1.7292
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6949 - val_loss: 1.5818
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5346 - val_loss: 1.4455
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4508 - val_loss: 1.3422
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3806 - val_loss: 1.2726
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3258 - val_loss: 1.2547
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3169 - val_loss: 1.2655
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2810 - val_loss: 1.2227
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2513 - val_loss: 1.1797
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2280 - val_loss: 1.1832
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2198 - val_loss: 1.1654
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2095 - val_loss: 1.1691
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1822 - val_loss: 1.1205
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1659 - val_loss: 1.1293
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1844 - val_loss: 1.1331
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1472 - val_loss: 1.1213
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1512 - val_loss: 1.1023
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1551 - val_loss: 1.1009
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1291 - val_loss: 1.0605
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1330 - val_loss: 1.0965
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1304 - val_loss: 1.0830
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1054 - val_loss: 1.0547
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1101 - val_loss: 1.0537
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1063 - val_loss: 1.0691
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0957 - val_loss: 1.0436
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0950 - val_loss: 1.0638
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1000 - val_loss: 1.0457
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0885 - val_loss: 1.0520
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0825 - val_loss: 1.0259
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0751 - val_loss: 1.0744
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0810 - val_loss: 1.0333
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0733 - val_loss: 1.0496
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0709 - val_loss: 1.0270
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0558 - val_loss: 1.0240
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0560 - val_loss: 1.0076
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0652 - val_loss: 1.0301
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0661 - val_loss: 1.0534
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0542 - val_loss: 0.9984
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0523 - val_loss: 1.0476
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0343 - val_loss: 1.0159
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0385 - val_loss: 1.0074
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0594 - val_loss: 1.0053
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0430 - val_loss: 0.9978
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0489 - val_loss: 1.0041
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0476 - val_loss: 0.9825
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0475 - val_loss: 0.9857
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0528 - val_loss: 1.0257
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0229 - val_loss: 1.0001
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0422 - val_loss: 0.9919
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0367 - val_loss: 1.0030
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0329 - val_loss: 1.0256
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0377 - val_loss: 0.9901
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0311 - val_loss: 0.9809
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0241 - val_loss: 1.0145
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0328 - val_loss: 0.9799
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0182 - val_loss: 0.9884
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0206 - val_loss: 1.0103
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0119 - val_loss: 1.0004
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0331 - val_loss: 0.9864
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0062 - val_loss: 0.9748
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0085 - val_loss: 1.0496
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0270 - val_loss: 0.9889
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0168 - val_loss: 0.9726
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0148 - val_loss: 0.9736
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0053 - val_loss: 0.9929
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0006 - val_loss: 0.9689
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0175 - val_loss: 0.9968
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0054 - val_loss: 0.9589
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0168 - val_loss: 0.9728
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0021 - val_loss: 0.9613
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0202 - val_loss: 1.0150
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0070 - val_loss: 0.9936
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0073 - val_loss: 0.9609
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0010 - val_loss: 0.9581
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0053 - val_loss: 0.9831
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9948 - val_loss: 1.0008
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0008 - val_loss: 0.9625
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9994 - val_loss: 0.9725
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0191 - val_loss: 0.9408
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9935 - val_loss: 0.9558
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9896 - val_loss: 0.9825
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0040 - val_loss: 0.9664
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9977 - val_loss: 0.9722
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0041 - val_loss: 0.9624
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9812 - val_loss: 0.9556
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9984 - val_loss: 0.9637
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9916 - val_loss: 0.9704
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9885 - val_loss: 0.9559
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0089 - val_loss: 0.9522
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9894 - val_loss: 0.9520
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0020 - val_loss: 0.9944
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9841 - val_loss: 0.9406
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9930 - val_loss: 0.9519
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9856 - val_loss: 0.9578
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9733 - val_loss: 0.9692
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9890 - val_loss: 0.9599
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9855 - val_loss: 0.9624
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.43 seconds.
    Calculating affinities...
    Calculated affinities in 0.02 seconds.
  Calculated graph and diffusion operator in 0.46 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 5.90 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.42 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 12.84 seconds.
Calculated PHATE in 19.63 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_126 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_127 (Dense)               (None, 32)           2080        dense_126[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_127[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_127[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_128 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_129 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_130 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_126 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_127 (Dense)               (None, 32)           2080        dense_126[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_127[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_127[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_420 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_420 ( [()]                 0           tf_op_layer_Shape_420[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_42 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_420[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_42[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_126 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_42 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_126[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_421 (TensorFl [(2,)]               0           tf_op_layer_Add_42[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_423 (TensorFl [(2,)]               0           tf_op_layer_Add_42[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_422 (TensorFl [(2,)]               0           tf_op_layer_Add_42[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_424 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_426 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_425 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_427 (TensorFl [(2,)]               0           tf_op_layer_Add_42[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_429 (TensorFl [(2,)]               0           tf_op_layer_Add_42[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_428 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_421 ( [()]                 0           tf_op_layer_Shape_421[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_423 ( [()]                 0           tf_op_layer_Shape_423[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_422 ( [()]                 0           tf_op_layer_Shape_422[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_424 ( [()]                 0           tf_op_layer_Shape_424[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_426 ( [()]                 0           tf_op_layer_Shape_426[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_425 ( [()]                 0           tf_op_layer_Shape_425[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_427 ( [()]                 0           tf_op_layer_Shape_427[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_429 ( [()]                 0           tf_op_layer_Shape_429[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_428 ( [()]                 0           tf_op_layer_Shape_428[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_252/shape ( [(3,)]               0           tf_op_layer_strided_slice_421[0][
                                                                 tf_op_layer_strided_slice_423[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_253/shape ( [(3,)]               0           tf_op_layer_strided_slice_422[0][
                                                                 tf_op_layer_strided_slice_423[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_254/shape ( [(3,)]               0           tf_op_layer_strided_slice_424[0][
                                                                 tf_op_layer_strided_slice_426[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_255/shape ( [(3,)]               0           tf_op_layer_strided_slice_425[0][
                                                                 tf_op_layer_strided_slice_426[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_256/shape ( [(3,)]               0           tf_op_layer_strided_slice_427[0][
                                                                 tf_op_layer_strided_slice_429[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_257/shape ( [(3,)]               0           tf_op_layer_strided_slice_428[0][
                                                                 tf_op_layer_strided_slice_429[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_252 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_42[0][0]         
                                                                 tf_op_layer_Reshape_252/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_252/multiples  [(3,)]               0           tf_op_layer_strided_slice_422[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_253 (Tensor [(1, None, None)]    0           tf_op_layer_Add_42[0][0]         
                                                                 tf_op_layer_Reshape_253/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_253/multiples  [(3,)]               0           tf_op_layer_strided_slice_421[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_254 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_254/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_254/multiples  [(3,)]               0           tf_op_layer_strided_slice_425[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_255 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_255/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_255/multiples  [(3,)]               0           tf_op_layer_strided_slice_424[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_256 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_42[0][0]         
                                                                 tf_op_layer_Reshape_256/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_256/multiples  [(3,)]               0           tf_op_layer_strided_slice_428[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_257 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_257/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_257/multiples  [(3,)]               0           tf_op_layer_strided_slice_427[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_252 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_252[0][0]    
                                                                 tf_op_layer_Tile_252/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_253 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_253[0][0]    
                                                                 tf_op_layer_Tile_253/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_254 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_254[0][0]    
                                                                 tf_op_layer_Tile_254/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_255 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_255[0][0]    
                                                                 tf_op_layer_Tile_255/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_256 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_256[0][0]    
                                                                 tf_op_layer_Tile_256/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_257 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_257[0][0]    
                                                                 tf_op_layer_Tile_257/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_210 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_252[0][0]       
                                                                 tf_op_layer_Tile_253[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_211 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_254[0][0]       
                                                                 tf_op_layer_Tile_255[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_212 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_256[0][0]       
                                                                 tf_op_layer_Tile_257[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_168 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_210[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_169 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_211[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_170 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_212[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_294 (TensorFlo [(None, None)]       0           tf_op_layer_Square_168[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_295 (TensorFlo [(None, None)]       0           tf_op_layer_Square_169[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_296 (TensorFlo [(None, None)]       0           tf_op_layer_Square_170[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_126 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_294[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_126 (TensorFlo [()]                 0           tf_op_layer_strided_slice_423[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_127 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_295[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_127 (TensorFlo [()]                 0           tf_op_layer_strided_slice_426[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_128 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_296[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_128 (TensorFlo [()]                 0           tf_op_layer_strided_slice_429[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_126 (Tensor [(None, None)]       0           tf_op_layer_Neg_126[0][0]        
                                                                 tf_op_layer_Cast_126[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_127 (Tensor [(None, None)]       0           tf_op_layer_Neg_127[0][0]        
                                                                 tf_op_layer_Cast_127[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_128 (Tensor [(None, None)]       0           tf_op_layer_Neg_128[0][0]        
                                                                 tf_op_layer_Cast_128[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_126 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_126[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_127 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_127[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_128 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_128[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_297 (TensorFlo [()]                 0           tf_op_layer_Exp_126[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_298 (TensorFlo [()]                 0           tf_op_layer_Exp_127[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_299 (TensorFlo [()]                 0           tf_op_layer_Exp_128[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_214 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_84 (TensorFlo [()]                 0           tf_op_layer_Mean_297[0][0]       
                                                                 tf_op_layer_Mean_298[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_127 (TensorFlow [()]                 0           tf_op_layer_Mean_299[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_171 (TensorF [(None, 97)]         0           tf_op_layer_Sub_214[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_213 (TensorFlow [()]                 0           tf_op_layer_AddV2_84[0][0]       
                                                                 tf_op_layer_Mul_127[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_42 (TensorFlowO [(None,)]            0           tf_op_layer_Square_171[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_128 (TensorFlow [()]                 0           tf_op_layer_Sub_213[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_85 (TensorFlo [(None,)]            0           tf_op_layer_Sum_42[0][0]         
                                                                 tf_op_layer_Mul_128[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_300 (TensorFlo [()]                 0           tf_op_layer_AddV2_85[0][0]       
__________________________________________________________________________________________________
add_loss_42 (AddLoss)           ()                   0           tf_op_layer_Mean_300[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 4s - loss: 10.3190WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0023s vs `on_train_batch_end` time: 0.3869s). Check your callbacks.
24/24 [==============================] - 1s 47ms/step - loss: 5.0121 - val_loss: 3.3862
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8896 - val_loss: 1.6218
Epoch 3/100
24/24 [==============================] - 0s 4ms/step - loss: 1.2794 - val_loss: 1.5741
Epoch 4/100
24/24 [==============================] - 0s 4ms/step - loss: 1.2039 - val_loss: 1.4235
Epoch 5/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1205 - val_loss: 1.4343
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0748 - val_loss: 1.3711
Epoch 7/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0590 - val_loss: 1.3922
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0339 - val_loss: 1.3440
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0157 - val_loss: 1.3171
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9872 - val_loss: 1.2728
Epoch 11/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9841 - val_loss: 1.2300
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9683 - val_loss: 1.2598
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9448 - val_loss: 1.1871
Epoch 14/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9403 - val_loss: 1.1975
Epoch 15/100
24/24 [==============================] - 0s 5ms/step - loss: 0.9161 - val_loss: 1.1543
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9011 - val_loss: 1.1107
Epoch 17/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8773 - val_loss: 1.0772
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8516 - val_loss: 1.0875
Epoch 19/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8333 - val_loss: 1.0603
Epoch 20/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8244 - val_loss: 1.0701
Epoch 21/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8095 - val_loss: 1.0573
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8016 - val_loss: 1.0426
Epoch 23/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7903 - val_loss: 1.0471
Epoch 24/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7867 - val_loss: 1.0411
Epoch 25/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7728 - val_loss: 1.0592
Epoch 26/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7752 - val_loss: 1.0693
Epoch 27/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7727 - val_loss: 1.1097
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7754 - val_loss: 1.0465
Epoch 29/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7704 - val_loss: 1.0752
Epoch 30/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7668 - val_loss: 1.0288
Epoch 31/100
24/24 [==============================] - ETA: 0s - loss: 0.781 - 0s 5ms/step - loss: 0.7632 - val_loss: 1.0612
Epoch 32/100
24/24 [==============================] - 0s 6ms/step - loss: 0.7567 - val_loss: 1.0455
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7783 - val_loss: 1.0195
Epoch 34/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7455 - val_loss: 1.0270
Epoch 35/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7491 - val_loss: 1.0074
Epoch 36/100
24/24 [==============================] - 0s 6ms/step - loss: 0.7479 - val_loss: 1.0451
Epoch 37/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7478 - val_loss: 1.0287
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7559 - val_loss: 1.0565
Epoch 39/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7425 - val_loss: 1.0161
Epoch 40/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7420 - val_loss: 1.0202
Epoch 41/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7482 - val_loss: 1.0235
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7451 - val_loss: 1.0307
Epoch 43/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7345 - val_loss: 1.0393
Epoch 44/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7314 - val_loss: 1.0028
Epoch 45/100
24/24 [==============================] - 0s 6ms/step - loss: 0.7300 - val_loss: 1.0001
Epoch 46/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7334 - val_loss: 0.9846
Epoch 47/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7292 - val_loss: 1.0169
Epoch 48/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7386 - val_loss: 1.0427
Epoch 49/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7282 - val_loss: 1.0152
Epoch 50/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7403 - val_loss: 1.0032
Epoch 51/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7349 - val_loss: 1.0160
Epoch 52/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7294 - val_loss: 1.0098
Epoch 53/100
24/24 [==============================] - 0s 7ms/step - loss: 0.7340 - val_loss: 1.0005
Epoch 54/100
24/24 [==============================] - 0s 7ms/step - loss: 0.7282 - val_loss: 1.0163
Epoch 55/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7236 - val_loss: 0.9965
Epoch 56/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7154 - val_loss: 0.9836
Epoch 57/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7211 - val_loss: 0.9941
Epoch 58/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7199 - val_loss: 0.9973
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7267 - val_loss: 0.9753
Epoch 60/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7187 - val_loss: 1.0365
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7201 - val_loss: 0.9849
Epoch 62/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7203 - val_loss: 1.0050
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7128 - val_loss: 1.0373
Epoch 64/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7130 - val_loss: 1.0050
Epoch 65/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7240 - val_loss: 0.9664
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7182 - val_loss: 1.0002
Epoch 67/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7218 - val_loss: 0.9772
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7180 - val_loss: 0.9938
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7148 - val_loss: 0.9849
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7169 - val_loss: 0.9648
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7200 - val_loss: 0.9674
Epoch 72/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7104 - val_loss: 0.9604
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7076 - val_loss: 0.9709
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7260 - val_loss: 0.9857
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7069 - val_loss: 0.9790
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7069 - val_loss: 0.9587
Epoch 77/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6967 - val_loss: 0.9983
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7091 - val_loss: 0.9910
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7077 - val_loss: 0.9981
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6990 - val_loss: 0.9670
Epoch 81/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6991 - val_loss: 1.0046
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7005 - val_loss: 0.9610
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7154 - val_loss: 0.9655
Epoch 84/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7040 - val_loss: 1.0183
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7020 - val_loss: 1.0028
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6994 - val_loss: 0.9850
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7064 - val_loss: 0.9864
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6987 - val_loss: 1.0091
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6930 - val_loss: 0.9802
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7102 - val_loss: 0.9972
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6957 - val_loss: 0.9674
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7004 - val_loss: 0.9834
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6958 - val_loss: 0.9437
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6828 - val_loss: 0.9625
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7041 - val_loss: 0.9765
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7007 - val_loss: 0.9781
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6910 - val_loss: 0.9530
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6831 - val_loss: 0.9742
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6867 - val_loss: 0.9662
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6848 - val_loss: 0.9872
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_131 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_430 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_430 ( [()]                 0           tf_op_layer_Shape_430[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_43 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_430[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_43[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_129 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_43 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_129[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_431 (TensorFl [(2,)]               0           tf_op_layer_Add_43[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_433 (TensorFl [(2,)]               0           tf_op_layer_Add_43[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_432 (TensorFl [(2,)]               0           tf_op_layer_Add_43[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_434 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_436 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_435 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_437 (TensorFl [(2,)]               0           tf_op_layer_Add_43[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_439 (TensorFl [(2,)]               0           tf_op_layer_Add_43[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_438 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_431 ( [()]                 0           tf_op_layer_Shape_431[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_433 ( [()]                 0           tf_op_layer_Shape_433[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_432 ( [()]                 0           tf_op_layer_Shape_432[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_434 ( [()]                 0           tf_op_layer_Shape_434[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_436 ( [()]                 0           tf_op_layer_Shape_436[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_435 ( [()]                 0           tf_op_layer_Shape_435[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_437 ( [()]                 0           tf_op_layer_Shape_437[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_439 ( [()]                 0           tf_op_layer_Shape_439[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_438 ( [()]                 0           tf_op_layer_Shape_438[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_258/shape ( [(3,)]               0           tf_op_layer_strided_slice_431[0][
                                                                 tf_op_layer_strided_slice_433[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_259/shape ( [(3,)]               0           tf_op_layer_strided_slice_432[0][
                                                                 tf_op_layer_strided_slice_433[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_260/shape ( [(3,)]               0           tf_op_layer_strided_slice_434[0][
                                                                 tf_op_layer_strided_slice_436[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_261/shape ( [(3,)]               0           tf_op_layer_strided_slice_435[0][
                                                                 tf_op_layer_strided_slice_436[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_262/shape ( [(3,)]               0           tf_op_layer_strided_slice_437[0][
                                                                 tf_op_layer_strided_slice_439[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_263/shape ( [(3,)]               0           tf_op_layer_strided_slice_438[0][
                                                                 tf_op_layer_strided_slice_439[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_258 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_43[0][0]         
                                                                 tf_op_layer_Reshape_258/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_258/multiples  [(3,)]               0           tf_op_layer_strided_slice_432[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_259 (Tensor [(1, None, None)]    0           tf_op_layer_Add_43[0][0]         
                                                                 tf_op_layer_Reshape_259/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_259/multiples  [(3,)]               0           tf_op_layer_strided_slice_431[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_260 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_260/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_260/multiples  [(3,)]               0           tf_op_layer_strided_slice_435[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_261 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_261/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_261/multiples  [(3,)]               0           tf_op_layer_strided_slice_434[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_262 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_43[0][0]         
                                                                 tf_op_layer_Reshape_262/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_262/multiples  [(3,)]               0           tf_op_layer_strided_slice_438[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_263 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_263/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_263/multiples  [(3,)]               0           tf_op_layer_strided_slice_437[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_258 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_258[0][0]    
                                                                 tf_op_layer_Tile_258/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_259 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_259[0][0]    
                                                                 tf_op_layer_Tile_259/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_260 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_260[0][0]    
                                                                 tf_op_layer_Tile_260/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_261 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_261[0][0]    
                                                                 tf_op_layer_Tile_261/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_262 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_262[0][0]    
                                                                 tf_op_layer_Tile_262/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_263 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_263[0][0]    
                                                                 tf_op_layer_Tile_263/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_215 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_258[0][0]       
                                                                 tf_op_layer_Tile_259[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_216 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_260[0][0]       
                                                                 tf_op_layer_Tile_261[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_217 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_262[0][0]       
                                                                 tf_op_layer_Tile_263[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_172 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_215[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_173 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_216[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_174 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_217[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_301 (TensorFlo [(None, None)]       0           tf_op_layer_Square_172[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_302 (TensorFlo [(None, None)]       0           tf_op_layer_Square_173[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_303 (TensorFlo [(None, None)]       0           tf_op_layer_Square_174[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_129 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_301[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_129 (TensorFlo [()]                 0           tf_op_layer_strided_slice_433[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_130 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_302[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_130 (TensorFlo [()]                 0           tf_op_layer_strided_slice_436[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_131 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_303[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_131 (TensorFlo [()]                 0           tf_op_layer_strided_slice_439[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_129 (Tensor [(None, None)]       0           tf_op_layer_Neg_129[0][0]        
                                                                 tf_op_layer_Cast_129[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_130 (Tensor [(None, None)]       0           tf_op_layer_Neg_130[0][0]        
                                                                 tf_op_layer_Cast_130[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_131 (Tensor [(None, None)]       0           tf_op_layer_Neg_131[0][0]        
                                                                 tf_op_layer_Cast_131[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_129 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_129[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_130 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_130[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_131 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_131[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_304 (TensorFlo [()]                 0           tf_op_layer_Exp_129[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_305 (TensorFlo [()]                 0           tf_op_layer_Exp_130[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_306 (TensorFlo [()]                 0           tf_op_layer_Exp_131[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_219 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_86 (TensorFlo [()]                 0           tf_op_layer_Mean_304[0][0]       
                                                                 tf_op_layer_Mean_305[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_130 (TensorFlow [()]                 0           tf_op_layer_Mean_306[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_175 (TensorF [(None, 97)]         0           tf_op_layer_Sub_219[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_218 (TensorFlow [()]                 0           tf_op_layer_AddV2_86[0][0]       
                                                                 tf_op_layer_Mul_130[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_43 (TensorFlowO [(None,)]            0           tf_op_layer_Square_175[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_131 (TensorFlow [()]                 0           tf_op_layer_Sub_218[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_87 (TensorFlo [(None,)]            0           tf_op_layer_Sum_43[0][0]         
                                                                 tf_op_layer_Mul_131[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_307 (TensorFlo [()]                 0           tf_op_layer_AddV2_87[0][0]       
__________________________________________________________________________________________________
add_loss_43 (AddLoss)           ()                   0           tf_op_layer_Mean_307[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 3s - loss: 11.0352WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.2681s). Check your callbacks.
24/24 [==============================] - 1s 30ms/step - loss: 8.1831 - val_loss: 4.9240
Epoch 2/100
24/24 [==============================] - 0s 4ms/step - loss: 3.9005 - val_loss: 2.5096
Epoch 3/100
24/24 [==============================] - 0s 4ms/step - loss: 2.3726 - val_loss: 1.9641
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.9760 - val_loss: 1.8568
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8342 - val_loss: 1.7330
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.7357 - val_loss: 1.6418
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6335 - val_loss: 1.5739
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6021 - val_loss: 1.5080
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5110 - val_loss: 1.4533
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4739 - val_loss: 1.3832
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4423 - val_loss: 1.3359
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3660 - val_loss: 1.2935
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3427 - val_loss: 1.2965
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2872 - val_loss: 1.2484
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2665 - val_loss: 1.2186
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2488 - val_loss: 1.2031
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2149 - val_loss: 1.1999
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1903 - val_loss: 1.1681
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1723 - val_loss: 1.1525
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1728 - val_loss: 1.1344
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1606 - val_loss: 1.0992
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1520 - val_loss: 1.1234
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1409 - val_loss: 1.1255
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1268 - val_loss: 1.1327
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1214 - val_loss: 1.1056
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1195 - val_loss: 1.0999
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1026 - val_loss: 1.0886
Epoch 28/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1065 - val_loss: 1.1176
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0975 - val_loss: 1.0707
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0800 - val_loss: 1.0885
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1003 - val_loss: 1.1120
Epoch 32/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0731 - val_loss: 1.0781
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0840 - val_loss: 1.0488
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0743 - val_loss: 1.0802
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0639 - val_loss: 1.0717
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0613 - val_loss: 1.0470
Epoch 37/100
24/24 [==============================] - 0s 6ms/step - loss: 1.0748 - val_loss: 1.0710
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0762 - val_loss: 1.0798
Epoch 39/100
24/24 [==============================] - 0s 8ms/step - loss: 1.0697 - val_loss: 1.0596
Epoch 40/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0556 - val_loss: 1.0895
Epoch 41/100
24/24 [==============================] - 0s 5ms/step - loss: 1.0578 - val_loss: 1.0601
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0355 - val_loss: 1.0403
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0586 - val_loss: 1.0612
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0481 - val_loss: 1.0303
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0461 - val_loss: 1.0427
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0515 - val_loss: 1.0343
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0343 - val_loss: 1.0586
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0269 - val_loss: 1.0507
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0445 - val_loss: 1.0225
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0352 - val_loss: 1.0384
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0513 - val_loss: 1.0392
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0329 - val_loss: 1.0230
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0352 - val_loss: 1.0334
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0306 - val_loss: 1.0168
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0306 - val_loss: 1.0141
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0332 - val_loss: 1.0252
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0250 - val_loss: 1.0275
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0144 - val_loss: 1.0271
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0364 - val_loss: 1.0257
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0178 - val_loss: 1.0278
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0286 - val_loss: 1.0346
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0245 - val_loss: 1.0137
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0148 - val_loss: 1.0059
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0164 - val_loss: 1.0321
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0242 - val_loss: 1.0341
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0210 - val_loss: 1.0417
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0123 - val_loss: 0.9987
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0159 - val_loss: 1.0179
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0137 - val_loss: 1.0299
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0144 - val_loss: 1.0233
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0098 - val_loss: 1.0354
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9983 - val_loss: 0.9979
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9977 - val_loss: 1.0083
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0126 - val_loss: 0.9869
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0035 - val_loss: 1.0337
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0046 - val_loss: 1.0392
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0037 - val_loss: 1.0030
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0094 - val_loss: 1.0167
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9966 - val_loss: 1.0298
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0065 - val_loss: 1.0108
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0045 - val_loss: 1.0120
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0070 - val_loss: 1.0292
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9910 - val_loss: 1.0095
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9877 - val_loss: 1.0191
Epoch 85/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9933 - val_loss: 1.0243
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9927 - val_loss: 0.9999
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9976 - val_loss: 0.9779
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9940 - val_loss: 1.0224
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0015 - val_loss: 0.9949
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9901 - val_loss: 1.0028
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9818 - val_loss: 0.9873
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9827 - val_loss: 1.0057
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9843 - val_loss: 1.0220
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9869 - val_loss: 1.0003
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9881 - val_loss: 0.9986
Epoch 96/100
24/24 [==============================] - 0s 5ms/step - loss: 0.9831 - val_loss: 1.0156
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9843 - val_loss: 1.0125
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9776 - val_loss: 0.9891
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9865 - val_loss: 0.9927
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9758 - val_loss: 0.9916
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.29 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.31 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.46 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.23 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 6.79 seconds.
Calculated PHATE in 8.80 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_132 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_133 (Dense)               (None, 32)           2080        dense_132[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_133[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_133[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_134 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_135 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_136 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_132 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_133 (Dense)               (None, 32)           2080        dense_132[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_133[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_133[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_440 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_440 ( [()]                 0           tf_op_layer_Shape_440[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_44 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_440[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_44[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_132 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_44 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_132[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_441 (TensorFl [(2,)]               0           tf_op_layer_Add_44[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_443 (TensorFl [(2,)]               0           tf_op_layer_Add_44[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_442 (TensorFl [(2,)]               0           tf_op_layer_Add_44[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_444 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_446 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_445 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_447 (TensorFl [(2,)]               0           tf_op_layer_Add_44[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_449 (TensorFl [(2,)]               0           tf_op_layer_Add_44[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_448 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_441 ( [()]                 0           tf_op_layer_Shape_441[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_443 ( [()]                 0           tf_op_layer_Shape_443[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_442 ( [()]                 0           tf_op_layer_Shape_442[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_444 ( [()]                 0           tf_op_layer_Shape_444[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_446 ( [()]                 0           tf_op_layer_Shape_446[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_445 ( [()]                 0           tf_op_layer_Shape_445[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_447 ( [()]                 0           tf_op_layer_Shape_447[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_449 ( [()]                 0           tf_op_layer_Shape_449[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_448 ( [()]                 0           tf_op_layer_Shape_448[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_264/shape ( [(3,)]               0           tf_op_layer_strided_slice_441[0][
                                                                 tf_op_layer_strided_slice_443[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_265/shape ( [(3,)]               0           tf_op_layer_strided_slice_442[0][
                                                                 tf_op_layer_strided_slice_443[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_266/shape ( [(3,)]               0           tf_op_layer_strided_slice_444[0][
                                                                 tf_op_layer_strided_slice_446[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_267/shape ( [(3,)]               0           tf_op_layer_strided_slice_445[0][
                                                                 tf_op_layer_strided_slice_446[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_268/shape ( [(3,)]               0           tf_op_layer_strided_slice_447[0][
                                                                 tf_op_layer_strided_slice_449[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_269/shape ( [(3,)]               0           tf_op_layer_strided_slice_448[0][
                                                                 tf_op_layer_strided_slice_449[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_264 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_44[0][0]         
                                                                 tf_op_layer_Reshape_264/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_264/multiples  [(3,)]               0           tf_op_layer_strided_slice_442[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_265 (Tensor [(1, None, None)]    0           tf_op_layer_Add_44[0][0]         
                                                                 tf_op_layer_Reshape_265/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_265/multiples  [(3,)]               0           tf_op_layer_strided_slice_441[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_266 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_266/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_266/multiples  [(3,)]               0           tf_op_layer_strided_slice_445[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_267 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_267/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_267/multiples  [(3,)]               0           tf_op_layer_strided_slice_444[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_268 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_44[0][0]         
                                                                 tf_op_layer_Reshape_268/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_268/multiples  [(3,)]               0           tf_op_layer_strided_slice_448[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_269 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_269/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_269/multiples  [(3,)]               0           tf_op_layer_strided_slice_447[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_264 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_264[0][0]    
                                                                 tf_op_layer_Tile_264/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_265 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_265[0][0]    
                                                                 tf_op_layer_Tile_265/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_266 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_266[0][0]    
                                                                 tf_op_layer_Tile_266/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_267 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_267[0][0]    
                                                                 tf_op_layer_Tile_267/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_268 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_268[0][0]    
                                                                 tf_op_layer_Tile_268/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_269 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_269[0][0]    
                                                                 tf_op_layer_Tile_269/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_220 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_264[0][0]       
                                                                 tf_op_layer_Tile_265[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_221 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_266[0][0]       
                                                                 tf_op_layer_Tile_267[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_222 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_268[0][0]       
                                                                 tf_op_layer_Tile_269[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_176 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_220[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_177 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_221[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_178 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_222[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_308 (TensorFlo [(None, None)]       0           tf_op_layer_Square_176[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_309 (TensorFlo [(None, None)]       0           tf_op_layer_Square_177[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_310 (TensorFlo [(None, None)]       0           tf_op_layer_Square_178[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_132 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_308[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_132 (TensorFlo [()]                 0           tf_op_layer_strided_slice_443[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_133 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_309[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_133 (TensorFlo [()]                 0           tf_op_layer_strided_slice_446[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_134 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_310[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_134 (TensorFlo [()]                 0           tf_op_layer_strided_slice_449[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_132 (Tensor [(None, None)]       0           tf_op_layer_Neg_132[0][0]        
                                                                 tf_op_layer_Cast_132[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_133 (Tensor [(None, None)]       0           tf_op_layer_Neg_133[0][0]        
                                                                 tf_op_layer_Cast_133[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_134 (Tensor [(None, None)]       0           tf_op_layer_Neg_134[0][0]        
                                                                 tf_op_layer_Cast_134[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_132 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_132[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_133 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_133[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_134 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_134[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_311 (TensorFlo [()]                 0           tf_op_layer_Exp_132[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_312 (TensorFlo [()]                 0           tf_op_layer_Exp_133[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_313 (TensorFlo [()]                 0           tf_op_layer_Exp_134[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_224 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_88 (TensorFlo [()]                 0           tf_op_layer_Mean_311[0][0]       
                                                                 tf_op_layer_Mean_312[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_133 (TensorFlow [()]                 0           tf_op_layer_Mean_313[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_179 (TensorF [(None, 97)]         0           tf_op_layer_Sub_224[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_223 (TensorFlow [()]                 0           tf_op_layer_AddV2_88[0][0]       
                                                                 tf_op_layer_Mul_133[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_44 (TensorFlowO [(None,)]            0           tf_op_layer_Square_179[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_134 (TensorFlow [()]                 0           tf_op_layer_Sub_223[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_89 (TensorFlo [(None,)]            0           tf_op_layer_Sum_44[0][0]         
                                                                 tf_op_layer_Mul_134[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_314 (TensorFlo [()]                 0           tf_op_layer_AddV2_89[0][0]       
__________________________________________________________________________________________________
add_loss_44 (AddLoss)           ()                   0           tf_op_layer_Mean_314[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.5115WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.1552s). Check your callbacks.
24/24 [==============================] - 0s 20ms/step - loss: 3.9741 - val_loss: 1.6542
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4126 - val_loss: 1.2181
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2183 - val_loss: 1.1144
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1257 - val_loss: 1.0161
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9924 - val_loss: 0.9123
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9172 - val_loss: 0.8832
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8739 - val_loss: 0.8307
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8672 - val_loss: 0.8105
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8455 - val_loss: 0.8096
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8239 - val_loss: 0.7818
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8132 - val_loss: 0.7976
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8103 - val_loss: 0.7975
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7965 - val_loss: 0.7951
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7941 - val_loss: 0.7697
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8161 - val_loss: 0.7973
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7743 - val_loss: 0.7699
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7709 - val_loss: 0.7358
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7682 - val_loss: 0.7787
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7762 - val_loss: 0.7465
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7672 - val_loss: 0.7454
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7680 - val_loss: 0.7654
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7564 - val_loss: 0.7576
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7709 - val_loss: 0.7414
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7603 - val_loss: 0.7397
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7475 - val_loss: 0.7429
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7598 - val_loss: 0.7303
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7442 - val_loss: 0.7585
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7485 - val_loss: 0.7439
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7378 - val_loss: 0.7630
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7353 - val_loss: 0.7875
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7337 - val_loss: 0.7355
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7394 - val_loss: 0.7259
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7344 - val_loss: 0.7407
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7337 - val_loss: 0.7375
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7313 - val_loss: 0.7328
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7192 - val_loss: 0.7202
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7268 - val_loss: 0.7607
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7179 - val_loss: 0.7053
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7241 - val_loss: 0.7494
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7252 - val_loss: 0.7075
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7218 - val_loss: 0.7029
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7114 - val_loss: 0.7359
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7175 - val_loss: 0.7640
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7184 - val_loss: 0.7220
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7203 - val_loss: 0.7094
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7104 - val_loss: 0.7318
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7056 - val_loss: 0.7504
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7133 - val_loss: 0.7238
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7125 - val_loss: 0.7024
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7010 - val_loss: 0.7094
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7071 - val_loss: 0.7139
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6963 - val_loss: 0.7279
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7022 - val_loss: 0.7070
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6943 - val_loss: 0.7013
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7040 - val_loss: 0.7174
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6946 - val_loss: 0.6941
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6925 - val_loss: 0.6997
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6991 - val_loss: 0.7104
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6987 - val_loss: 0.7116
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6900 - val_loss: 0.7330
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6863 - val_loss: 0.6914
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6934 - val_loss: 0.7039
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6951 - val_loss: 0.7478
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6907 - val_loss: 0.7042
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6991 - val_loss: 0.6806
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6917 - val_loss: 0.7117
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6918 - val_loss: 0.7136
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6900 - val_loss: 0.7055
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6887 - val_loss: 0.7101
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7053 - val_loss: 0.6908
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6998 - val_loss: 0.6869
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6908 - val_loss: 0.6907
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6863 - val_loss: 0.6866
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6826 - val_loss: 0.7521
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6822 - val_loss: 0.7210
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6886 - val_loss: 0.7059
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6845 - val_loss: 0.6744
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6841 - val_loss: 0.6844
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6798 - val_loss: 0.6848
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6767 - val_loss: 0.7048
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6744 - val_loss: 0.6778
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6955 - val_loss: 0.6774
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6784 - val_loss: 0.6999
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6890 - val_loss: 0.7156
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6833 - val_loss: 0.7065
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6775 - val_loss: 0.6913
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6745 - val_loss: 0.6697
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6719 - val_loss: 0.6844
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6824 - val_loss: 0.6823
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6710 - val_loss: 0.6692
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6753 - val_loss: 0.6986
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6814 - val_loss: 0.6724
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6760 - val_loss: 0.6680
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6741 - val_loss: 0.7051
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6773 - val_loss: 0.6696
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6725 - val_loss: 0.6676
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6654 - val_loss: 0.6718
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6784 - val_loss: 0.6804
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6610 - val_loss: 0.7052
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6676 - val_loss: 0.6630
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_137 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_450 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_450 ( [()]                 0           tf_op_layer_Shape_450[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_45 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_450[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_45[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_135 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_45 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_135[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_451 (TensorFl [(2,)]               0           tf_op_layer_Add_45[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_453 (TensorFl [(2,)]               0           tf_op_layer_Add_45[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_452 (TensorFl [(2,)]               0           tf_op_layer_Add_45[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_454 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_456 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_455 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_457 (TensorFl [(2,)]               0           tf_op_layer_Add_45[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_459 (TensorFl [(2,)]               0           tf_op_layer_Add_45[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_458 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_451 ( [()]                 0           tf_op_layer_Shape_451[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_453 ( [()]                 0           tf_op_layer_Shape_453[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_452 ( [()]                 0           tf_op_layer_Shape_452[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_454 ( [()]                 0           tf_op_layer_Shape_454[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_456 ( [()]                 0           tf_op_layer_Shape_456[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_455 ( [()]                 0           tf_op_layer_Shape_455[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_457 ( [()]                 0           tf_op_layer_Shape_457[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_459 ( [()]                 0           tf_op_layer_Shape_459[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_458 ( [()]                 0           tf_op_layer_Shape_458[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_270/shape ( [(3,)]               0           tf_op_layer_strided_slice_451[0][
                                                                 tf_op_layer_strided_slice_453[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_271/shape ( [(3,)]               0           tf_op_layer_strided_slice_452[0][
                                                                 tf_op_layer_strided_slice_453[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_272/shape ( [(3,)]               0           tf_op_layer_strided_slice_454[0][
                                                                 tf_op_layer_strided_slice_456[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_273/shape ( [(3,)]               0           tf_op_layer_strided_slice_455[0][
                                                                 tf_op_layer_strided_slice_456[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_274/shape ( [(3,)]               0           tf_op_layer_strided_slice_457[0][
                                                                 tf_op_layer_strided_slice_459[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_275/shape ( [(3,)]               0           tf_op_layer_strided_slice_458[0][
                                                                 tf_op_layer_strided_slice_459[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_270 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_45[0][0]         
                                                                 tf_op_layer_Reshape_270/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_270/multiples  [(3,)]               0           tf_op_layer_strided_slice_452[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_271 (Tensor [(1, None, None)]    0           tf_op_layer_Add_45[0][0]         
                                                                 tf_op_layer_Reshape_271/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_271/multiples  [(3,)]               0           tf_op_layer_strided_slice_451[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_272 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_272/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_272/multiples  [(3,)]               0           tf_op_layer_strided_slice_455[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_273 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_273/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_273/multiples  [(3,)]               0           tf_op_layer_strided_slice_454[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_274 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_45[0][0]         
                                                                 tf_op_layer_Reshape_274/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_274/multiples  [(3,)]               0           tf_op_layer_strided_slice_458[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_275 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_275/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_275/multiples  [(3,)]               0           tf_op_layer_strided_slice_457[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_270 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_270[0][0]    
                                                                 tf_op_layer_Tile_270/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_271 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_271[0][0]    
                                                                 tf_op_layer_Tile_271/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_272 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_272[0][0]    
                                                                 tf_op_layer_Tile_272/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_273 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_273[0][0]    
                                                                 tf_op_layer_Tile_273/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_274 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_274[0][0]    
                                                                 tf_op_layer_Tile_274/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_275 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_275[0][0]    
                                                                 tf_op_layer_Tile_275/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_225 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_270[0][0]       
                                                                 tf_op_layer_Tile_271[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_226 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_272[0][0]       
                                                                 tf_op_layer_Tile_273[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_227 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_274[0][0]       
                                                                 tf_op_layer_Tile_275[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_180 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_225[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_181 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_226[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_182 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_227[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_315 (TensorFlo [(None, None)]       0           tf_op_layer_Square_180[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_316 (TensorFlo [(None, None)]       0           tf_op_layer_Square_181[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_317 (TensorFlo [(None, None)]       0           tf_op_layer_Square_182[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_135 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_315[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_135 (TensorFlo [()]                 0           tf_op_layer_strided_slice_453[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_136 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_316[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_136 (TensorFlo [()]                 0           tf_op_layer_strided_slice_456[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_137 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_317[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_137 (TensorFlo [()]                 0           tf_op_layer_strided_slice_459[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_135 (Tensor [(None, None)]       0           tf_op_layer_Neg_135[0][0]        
                                                                 tf_op_layer_Cast_135[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_136 (Tensor [(None, None)]       0           tf_op_layer_Neg_136[0][0]        
                                                                 tf_op_layer_Cast_136[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_137 (Tensor [(None, None)]       0           tf_op_layer_Neg_137[0][0]        
                                                                 tf_op_layer_Cast_137[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_135 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_135[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_136 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_136[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_137 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_137[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_318 (TensorFlo [()]                 0           tf_op_layer_Exp_135[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_319 (TensorFlo [()]                 0           tf_op_layer_Exp_136[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_320 (TensorFlo [()]                 0           tf_op_layer_Exp_137[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_229 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_90 (TensorFlo [()]                 0           tf_op_layer_Mean_318[0][0]       
                                                                 tf_op_layer_Mean_319[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_136 (TensorFlow [()]                 0           tf_op_layer_Mean_320[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_183 (TensorF [(None, 97)]         0           tf_op_layer_Sub_229[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_228 (TensorFlow [()]                 0           tf_op_layer_AddV2_90[0][0]       
                                                                 tf_op_layer_Mul_136[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_45 (TensorFlowO [(None,)]            0           tf_op_layer_Square_183[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_137 (TensorFlow [()]                 0           tf_op_layer_Sub_228[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_91 (TensorFlo [(None,)]            0           tf_op_layer_Sum_45[0][0]         
                                                                 tf_op_layer_Mul_137[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_321 (TensorFlo [()]                 0           tf_op_layer_AddV2_91[0][0]       
__________________________________________________________________________________________________
add_loss_45 (AddLoss)           ()                   0           tf_op_layer_Mean_321[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.4896WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0014s vs `on_train_batch_end` time: 0.1052s). Check your callbacks.
24/24 [==============================] - 0s 15ms/step - loss: 7.8901 - val_loss: 5.2172
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.6358 - val_loss: 3.7059
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 3.2504 - val_loss: 2.4891
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.2730 - val_loss: 1.9693
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8937 - val_loss: 1.7980
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7430 - val_loss: 1.6348
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6668 - val_loss: 1.5977
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5556 - val_loss: 1.5384
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5017 - val_loss: 1.4840
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4570 - val_loss: 1.4467
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3952 - val_loss: 1.3865
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3338 - val_loss: 1.4112
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3188 - val_loss: 1.3277
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2831 - val_loss: 1.2625
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2439 - val_loss: 1.2351
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2281 - val_loss: 1.1894
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2086 - val_loss: 1.1618
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1953 - val_loss: 1.1700
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1717 - val_loss: 1.1933
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1623 - val_loss: 1.1438
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1529 - val_loss: 1.0710
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1353 - val_loss: 1.1190
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1261 - val_loss: 1.0696
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1242 - val_loss: 1.0410
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1298 - val_loss: 1.0942
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1038 - val_loss: 1.0630
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0927 - val_loss: 1.0510
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1191 - val_loss: 1.0817
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0917 - val_loss: 1.0841
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0685 - val_loss: 1.0452
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0834 - val_loss: 1.0103
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0673 - val_loss: 1.1156
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0728 - val_loss: 1.0767
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0571 - val_loss: 1.0397
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0655 - val_loss: 1.0608
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0632 - val_loss: 1.0049
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0567 - val_loss: 1.0476
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0537 - val_loss: 1.0215
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0513 - val_loss: 1.0512
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0567 - val_loss: 0.9953
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0432 - val_loss: 1.0124
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0327 - val_loss: 1.0637
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0301 - val_loss: 1.0365
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0320 - val_loss: 1.0031
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0379 - val_loss: 1.0034
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0312 - val_loss: 1.0265
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0332 - val_loss: 1.0368
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0387 - val_loss: 0.9974
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0244 - val_loss: 1.0538
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0445 - val_loss: 0.9994
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0258 - val_loss: 1.0153
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0263 - val_loss: 1.0300
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0407 - val_loss: 1.0117
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0249 - val_loss: 1.0164
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0248 - val_loss: 1.0323
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0151 - val_loss: 1.0425
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0214 - val_loss: 0.9756
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0253 - val_loss: 1.0247
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0049 - val_loss: 0.9926
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0220 - val_loss: 0.9958
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0265 - val_loss: 1.0362
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0132 - val_loss: 0.9843
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0081 - val_loss: 1.0023
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0126 - val_loss: 0.9868
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9994 - val_loss: 0.9881
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0184 - val_loss: 1.0152
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0160 - val_loss: 1.0176
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9975 - val_loss: 1.0233
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0147 - val_loss: 0.9976
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9980 - val_loss: 0.9892
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0008 - val_loss: 0.9991
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0114 - val_loss: 0.9783
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0033 - val_loss: 1.0158
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9912 - val_loss: 0.9865
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9986 - val_loss: 1.0124
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9860 - val_loss: 1.0379
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9913 - val_loss: 0.9802
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0005 - val_loss: 0.9628
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9972 - val_loss: 1.0403
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0007 - val_loss: 0.9875
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9929 - val_loss: 0.9902
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9839 - val_loss: 0.9493
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9845 - val_loss: 1.0306
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9871 - val_loss: 0.9790
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9863 - val_loss: 0.9628
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0072 - val_loss: 0.9498
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9891 - val_loss: 0.9678
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9854 - val_loss: 0.9608
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9971 - val_loss: 0.9990
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9785 - val_loss: 0.9685
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9879 - val_loss: 0.9748
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9841 - val_loss: 1.0317
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9822 - val_loss: 0.9983
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9677 - val_loss: 0.9978
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9725 - val_loss: 0.9600
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9864 - val_loss: 0.9980
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9797 - val_loss: 0.9784
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9802 - val_loss: 0.9703
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9762 - val_loss: 0.9898
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9779 - val_loss: 0.9780
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.32 seconds.
    Calculating affinities...
    Calculated affinities in 0.02 seconds.
  Calculated graph and diffusion operator in 0.35 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.50 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.22 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 8.74 seconds.
Calculated PHATE in 10.82 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_138 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_139 (Dense)               (None, 32)           2080        dense_138[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_139[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_139[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_140 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_141 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_142 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_138 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_139 (Dense)               (None, 32)           2080        dense_138[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_139[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_139[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_460 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_460 ( [()]                 0           tf_op_layer_Shape_460[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_46 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_460[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_46[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_138 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_46 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_138[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_461 (TensorFl [(2,)]               0           tf_op_layer_Add_46[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_463 (TensorFl [(2,)]               0           tf_op_layer_Add_46[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_462 (TensorFl [(2,)]               0           tf_op_layer_Add_46[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_464 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_466 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_465 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_467 (TensorFl [(2,)]               0           tf_op_layer_Add_46[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_469 (TensorFl [(2,)]               0           tf_op_layer_Add_46[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_468 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_461 ( [()]                 0           tf_op_layer_Shape_461[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_463 ( [()]                 0           tf_op_layer_Shape_463[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_462 ( [()]                 0           tf_op_layer_Shape_462[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_464 ( [()]                 0           tf_op_layer_Shape_464[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_466 ( [()]                 0           tf_op_layer_Shape_466[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_465 ( [()]                 0           tf_op_layer_Shape_465[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_467 ( [()]                 0           tf_op_layer_Shape_467[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_469 ( [()]                 0           tf_op_layer_Shape_469[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_468 ( [()]                 0           tf_op_layer_Shape_468[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_276/shape ( [(3,)]               0           tf_op_layer_strided_slice_461[0][
                                                                 tf_op_layer_strided_slice_463[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_277/shape ( [(3,)]               0           tf_op_layer_strided_slice_462[0][
                                                                 tf_op_layer_strided_slice_463[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_278/shape ( [(3,)]               0           tf_op_layer_strided_slice_464[0][
                                                                 tf_op_layer_strided_slice_466[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_279/shape ( [(3,)]               0           tf_op_layer_strided_slice_465[0][
                                                                 tf_op_layer_strided_slice_466[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_280/shape ( [(3,)]               0           tf_op_layer_strided_slice_467[0][
                                                                 tf_op_layer_strided_slice_469[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_281/shape ( [(3,)]               0           tf_op_layer_strided_slice_468[0][
                                                                 tf_op_layer_strided_slice_469[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_276 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_46[0][0]         
                                                                 tf_op_layer_Reshape_276/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_276/multiples  [(3,)]               0           tf_op_layer_strided_slice_462[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_277 (Tensor [(1, None, None)]    0           tf_op_layer_Add_46[0][0]         
                                                                 tf_op_layer_Reshape_277/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_277/multiples  [(3,)]               0           tf_op_layer_strided_slice_461[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_278 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_278/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_278/multiples  [(3,)]               0           tf_op_layer_strided_slice_465[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_279 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_279/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_279/multiples  [(3,)]               0           tf_op_layer_strided_slice_464[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_280 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_46[0][0]         
                                                                 tf_op_layer_Reshape_280/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_280/multiples  [(3,)]               0           tf_op_layer_strided_slice_468[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_281 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_281/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_281/multiples  [(3,)]               0           tf_op_layer_strided_slice_467[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_276 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_276[0][0]    
                                                                 tf_op_layer_Tile_276/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_277 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_277[0][0]    
                                                                 tf_op_layer_Tile_277/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_278 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_278[0][0]    
                                                                 tf_op_layer_Tile_278/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_279 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_279[0][0]    
                                                                 tf_op_layer_Tile_279/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_280 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_280[0][0]    
                                                                 tf_op_layer_Tile_280/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_281 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_281[0][0]    
                                                                 tf_op_layer_Tile_281/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_230 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_276[0][0]       
                                                                 tf_op_layer_Tile_277[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_231 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_278[0][0]       
                                                                 tf_op_layer_Tile_279[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_232 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_280[0][0]       
                                                                 tf_op_layer_Tile_281[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_184 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_230[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_185 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_231[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_186 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_232[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_322 (TensorFlo [(None, None)]       0           tf_op_layer_Square_184[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_323 (TensorFlo [(None, None)]       0           tf_op_layer_Square_185[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_324 (TensorFlo [(None, None)]       0           tf_op_layer_Square_186[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_138 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_322[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_138 (TensorFlo [()]                 0           tf_op_layer_strided_slice_463[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_139 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_323[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_139 (TensorFlo [()]                 0           tf_op_layer_strided_slice_466[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_140 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_324[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_140 (TensorFlo [()]                 0           tf_op_layer_strided_slice_469[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_138 (Tensor [(None, None)]       0           tf_op_layer_Neg_138[0][0]        
                                                                 tf_op_layer_Cast_138[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_139 (Tensor [(None, None)]       0           tf_op_layer_Neg_139[0][0]        
                                                                 tf_op_layer_Cast_139[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_140 (Tensor [(None, None)]       0           tf_op_layer_Neg_140[0][0]        
                                                                 tf_op_layer_Cast_140[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_138 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_138[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_139 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_139[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_140 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_140[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_325 (TensorFlo [()]                 0           tf_op_layer_Exp_138[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_326 (TensorFlo [()]                 0           tf_op_layer_Exp_139[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_327 (TensorFlo [()]                 0           tf_op_layer_Exp_140[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_234 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_92 (TensorFlo [()]                 0           tf_op_layer_Mean_325[0][0]       
                                                                 tf_op_layer_Mean_326[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_139 (TensorFlow [()]                 0           tf_op_layer_Mean_327[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_187 (TensorF [(None, 97)]         0           tf_op_layer_Sub_234[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_233 (TensorFlow [()]                 0           tf_op_layer_AddV2_92[0][0]       
                                                                 tf_op_layer_Mul_139[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_46 (TensorFlowO [(None,)]            0           tf_op_layer_Square_187[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_140 (TensorFlow [()]                 0           tf_op_layer_Sub_233[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_93 (TensorFlo [(None,)]            0           tf_op_layer_Sum_46[0][0]         
                                                                 tf_op_layer_Mul_140[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_328 (TensorFlo [()]                 0           tf_op_layer_AddV2_93[0][0]       
__________________________________________________________________________________________________
add_loss_46 (AddLoss)           ()                   0           tf_op_layer_Mean_328[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.2204WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0019s vs `on_train_batch_end` time: 0.1907s). Check your callbacks.
24/24 [==============================] - 1s 22ms/step - loss: 4.1585 - val_loss: 1.9979
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4760 - val_loss: 1.3712
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1771 - val_loss: 1.1875
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0530 - val_loss: 1.0945
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9217 - val_loss: 0.9732
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8837 - val_loss: 0.9354
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8608 - val_loss: 0.9100
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8392 - val_loss: 0.9107
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8256 - val_loss: 0.8641
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8236 - val_loss: 0.9024
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8334 - val_loss: 0.8587
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8060 - val_loss: 0.8600
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8008 - val_loss: 0.8808
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8134 - val_loss: 0.8381
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7994 - val_loss: 0.8348
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7903 - val_loss: 0.8254
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7755 - val_loss: 0.8192
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7784 - val_loss: 0.8173
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7750 - val_loss: 0.8608
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7742 - val_loss: 0.8108
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7723 - val_loss: 0.8304
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7761 - val_loss: 0.8108
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7565 - val_loss: 0.8229
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7655 - val_loss: 0.8198
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7482 - val_loss: 0.8226
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7509 - val_loss: 0.7951
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7458 - val_loss: 0.7949
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7496 - val_loss: 0.8323
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7375 - val_loss: 0.8200
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7347 - val_loss: 0.7960
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7328 - val_loss: 0.7889
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7364 - val_loss: 0.7898
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7403 - val_loss: 0.7856
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7254 - val_loss: 0.7787
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7296 - val_loss: 0.7871
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7311 - val_loss: 0.7944
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7252 - val_loss: 0.7868
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7207 - val_loss: 0.7639
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7236 - val_loss: 0.7745
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7252 - val_loss: 0.7968
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7166 - val_loss: 0.8015
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7237 - val_loss: 0.7993
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7222 - val_loss: 0.7554
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7218 - val_loss: 0.8027
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7166 - val_loss: 0.7561
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7196 - val_loss: 0.7682
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7161 - val_loss: 0.8046
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7079 - val_loss: 0.7744
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7073 - val_loss: 0.7815
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7079 - val_loss: 0.8116
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7123 - val_loss: 0.7842
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7017 - val_loss: 0.8027
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7054 - val_loss: 0.7652
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7084 - val_loss: 0.7767
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7089 - val_loss: 0.7692
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7045 - val_loss: 0.7825
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6941 - val_loss: 0.7594
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6947 - val_loss: 0.7703
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7007 - val_loss: 0.7882
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6991 - val_loss: 0.7962
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6863 - val_loss: 0.7649
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6969 - val_loss: 0.7991
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6973 - val_loss: 0.7822
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6970 - val_loss: 0.7550
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7069 - val_loss: 0.7751
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6917 - val_loss: 0.7685
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6839 - val_loss: 0.7608
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6878 - val_loss: 0.7842
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6891 - val_loss: 0.7742
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6906 - val_loss: 0.7609
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6840 - val_loss: 0.7544
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6927 - val_loss: 0.7782
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6866 - val_loss: 0.7684
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6839 - val_loss: 0.8051
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6832 - val_loss: 0.7809
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6862 - val_loss: 0.7730
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6856 - val_loss: 0.7402
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6782 - val_loss: 0.7529
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6862 - val_loss: 0.7588
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6805 - val_loss: 0.7561
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6705 - val_loss: 0.7757
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6781 - val_loss: 0.7731
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6797 - val_loss: 0.7956
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6784 - val_loss: 0.7643
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6715 - val_loss: 0.7403
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6687 - val_loss: 0.7586
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6734 - val_loss: 0.7565
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6620 - val_loss: 0.7680
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6697 - val_loss: 0.7322
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6779 - val_loss: 0.7489
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6673 - val_loss: 0.7512
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6784 - val_loss: 0.7690
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6785 - val_loss: 0.7461
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6728 - val_loss: 0.7386
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6593 - val_loss: 0.7665
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6649 - val_loss: 0.7785
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6644 - val_loss: 0.7689
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6631 - val_loss: 0.7533
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6627 - val_loss: 0.7630
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6709 - val_loss: 0.7344
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_143 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_470 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_470 ( [()]                 0           tf_op_layer_Shape_470[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_47 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_470[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_47[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_141 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_47 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_141[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_471 (TensorFl [(2,)]               0           tf_op_layer_Add_47[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_473 (TensorFl [(2,)]               0           tf_op_layer_Add_47[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_472 (TensorFl [(2,)]               0           tf_op_layer_Add_47[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_474 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_476 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_475 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_477 (TensorFl [(2,)]               0           tf_op_layer_Add_47[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_479 (TensorFl [(2,)]               0           tf_op_layer_Add_47[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_478 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_471 ( [()]                 0           tf_op_layer_Shape_471[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_473 ( [()]                 0           tf_op_layer_Shape_473[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_472 ( [()]                 0           tf_op_layer_Shape_472[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_474 ( [()]                 0           tf_op_layer_Shape_474[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_476 ( [()]                 0           tf_op_layer_Shape_476[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_475 ( [()]                 0           tf_op_layer_Shape_475[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_477 ( [()]                 0           tf_op_layer_Shape_477[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_479 ( [()]                 0           tf_op_layer_Shape_479[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_478 ( [()]                 0           tf_op_layer_Shape_478[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_282/shape ( [(3,)]               0           tf_op_layer_strided_slice_471[0][
                                                                 tf_op_layer_strided_slice_473[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_283/shape ( [(3,)]               0           tf_op_layer_strided_slice_472[0][
                                                                 tf_op_layer_strided_slice_473[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_284/shape ( [(3,)]               0           tf_op_layer_strided_slice_474[0][
                                                                 tf_op_layer_strided_slice_476[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_285/shape ( [(3,)]               0           tf_op_layer_strided_slice_475[0][
                                                                 tf_op_layer_strided_slice_476[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_286/shape ( [(3,)]               0           tf_op_layer_strided_slice_477[0][
                                                                 tf_op_layer_strided_slice_479[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_287/shape ( [(3,)]               0           tf_op_layer_strided_slice_478[0][
                                                                 tf_op_layer_strided_slice_479[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_282 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_47[0][0]         
                                                                 tf_op_layer_Reshape_282/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_282/multiples  [(3,)]               0           tf_op_layer_strided_slice_472[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_283 (Tensor [(1, None, None)]    0           tf_op_layer_Add_47[0][0]         
                                                                 tf_op_layer_Reshape_283/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_283/multiples  [(3,)]               0           tf_op_layer_strided_slice_471[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_284 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_284/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_284/multiples  [(3,)]               0           tf_op_layer_strided_slice_475[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_285 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_285/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_285/multiples  [(3,)]               0           tf_op_layer_strided_slice_474[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_286 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_47[0][0]         
                                                                 tf_op_layer_Reshape_286/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_286/multiples  [(3,)]               0           tf_op_layer_strided_slice_478[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_287 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_287/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_287/multiples  [(3,)]               0           tf_op_layer_strided_slice_477[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_282 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_282[0][0]    
                                                                 tf_op_layer_Tile_282/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_283 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_283[0][0]    
                                                                 tf_op_layer_Tile_283/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_284 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_284[0][0]    
                                                                 tf_op_layer_Tile_284/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_285 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_285[0][0]    
                                                                 tf_op_layer_Tile_285/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_286 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_286[0][0]    
                                                                 tf_op_layer_Tile_286/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_287 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_287[0][0]    
                                                                 tf_op_layer_Tile_287/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_235 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_282[0][0]       
                                                                 tf_op_layer_Tile_283[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_236 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_284[0][0]       
                                                                 tf_op_layer_Tile_285[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_237 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_286[0][0]       
                                                                 tf_op_layer_Tile_287[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_188 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_235[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_189 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_236[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_190 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_237[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_329 (TensorFlo [(None, None)]       0           tf_op_layer_Square_188[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_330 (TensorFlo [(None, None)]       0           tf_op_layer_Square_189[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_331 (TensorFlo [(None, None)]       0           tf_op_layer_Square_190[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_141 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_329[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_141 (TensorFlo [()]                 0           tf_op_layer_strided_slice_473[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_142 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_330[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_142 (TensorFlo [()]                 0           tf_op_layer_strided_slice_476[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_143 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_331[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_143 (TensorFlo [()]                 0           tf_op_layer_strided_slice_479[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_141 (Tensor [(None, None)]       0           tf_op_layer_Neg_141[0][0]        
                                                                 tf_op_layer_Cast_141[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_142 (Tensor [(None, None)]       0           tf_op_layer_Neg_142[0][0]        
                                                                 tf_op_layer_Cast_142[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_143 (Tensor [(None, None)]       0           tf_op_layer_Neg_143[0][0]        
                                                                 tf_op_layer_Cast_143[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_141 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_141[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_142 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_142[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_143 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_143[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_332 (TensorFlo [()]                 0           tf_op_layer_Exp_141[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_333 (TensorFlo [()]                 0           tf_op_layer_Exp_142[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_334 (TensorFlo [()]                 0           tf_op_layer_Exp_143[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_239 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_94 (TensorFlo [()]                 0           tf_op_layer_Mean_332[0][0]       
                                                                 tf_op_layer_Mean_333[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_142 (TensorFlow [()]                 0           tf_op_layer_Mean_334[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_191 (TensorF [(None, 97)]         0           tf_op_layer_Sub_239[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_238 (TensorFlow [()]                 0           tf_op_layer_AddV2_94[0][0]       
                                                                 tf_op_layer_Mul_142[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_47 (TensorFlowO [(None,)]            0           tf_op_layer_Square_191[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_143 (TensorFlow [()]                 0           tf_op_layer_Sub_238[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_95 (TensorFlo [(None,)]            0           tf_op_layer_Sum_47[0][0]         
                                                                 tf_op_layer_Mul_143[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_335 (TensorFlo [()]                 0           tf_op_layer_AddV2_95[0][0]       
__________________________________________________________________________________________________
add_loss_47 (AddLoss)           ()                   0           tf_op_layer_Mean_335[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.8833WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.1398s). Check your callbacks.
24/24 [==============================] - 0s 17ms/step - loss: 7.7523 - val_loss: 5.3993
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.1579 - val_loss: 3.5142
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.6964 - val_loss: 2.5697
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.0768 - val_loss: 2.1106
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7973 - val_loss: 1.9470
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6266 - val_loss: 1.7640
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5012 - val_loss: 1.6397
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4154 - val_loss: 1.5440
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3532 - val_loss: 1.4908
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3086 - val_loss: 1.4480
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2736 - val_loss: 1.4319
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2504 - val_loss: 1.4160
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2114 - val_loss: 1.3671
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2042 - val_loss: 1.3420
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2010 - val_loss: 1.3175
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1689 - val_loss: 1.3017
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1671 - val_loss: 1.3166
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1538 - val_loss: 1.3076
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1312 - val_loss: 1.3073
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1242 - val_loss: 1.2844
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1136 - val_loss: 1.2636
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1166 - val_loss: 1.2993
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1162 - val_loss: 1.2654
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1079 - val_loss: 1.2596
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0961 - val_loss: 1.2377
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0943 - val_loss: 1.2181
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0845 - val_loss: 1.2389
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0903 - val_loss: 1.2256
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0914 - val_loss: 1.2141
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0777 - val_loss: 1.2255
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0609 - val_loss: 1.2309
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0661 - val_loss: 1.2089
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0713 - val_loss: 1.2118
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0645 - val_loss: 1.2209
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0626 - val_loss: 1.2193
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0787 - val_loss: 1.2052
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0634 - val_loss: 1.1913
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0504 - val_loss: 1.1961
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0604 - val_loss: 1.1854
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0536 - val_loss: 1.1972
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0500 - val_loss: 1.1761
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0469 - val_loss: 1.1749
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0445 - val_loss: 1.1786
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0418 - val_loss: 1.1829
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0265 - val_loss: 1.1637
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0387 - val_loss: 1.1734
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0602 - val_loss: 1.1537
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0386 - val_loss: 1.1839
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0393 - val_loss: 1.1897
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0277 - val_loss: 1.1971
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0359 - val_loss: 1.1713
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0421 - val_loss: 1.1861
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0229 - val_loss: 1.1758
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0238 - val_loss: 1.1664
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0209 - val_loss: 1.1647
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0316 - val_loss: 1.1719
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0288 - val_loss: 1.1524
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0357 - val_loss: 1.1504
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0308 - val_loss: 1.1798
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0249 - val_loss: 1.1541
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0129 - val_loss: 1.1462
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0136 - val_loss: 1.1363
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0178 - val_loss: 1.1624
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0163 - val_loss: 1.1797
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0184 - val_loss: 1.1615
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0132 - val_loss: 1.1687
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0034 - val_loss: 1.1648
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0363 - val_loss: 1.1643
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0014 - val_loss: 1.1463
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0062 - val_loss: 1.1532
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0137 - val_loss: 1.1651
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9997 - val_loss: 1.1765
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0039 - val_loss: 1.1464
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0045 - val_loss: 1.1419
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0044 - val_loss: 1.1368
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0072 - val_loss: 1.1677
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9976 - val_loss: 1.1528
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9958 - val_loss: 1.1484
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0099 - val_loss: 1.1513
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0109 - val_loss: 1.1580
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9987 - val_loss: 1.1762
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0053 - val_loss: 1.1281
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9951 - val_loss: 1.1343
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9980 - val_loss: 1.1410
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9902 - val_loss: 1.1372
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9844 - val_loss: 1.1367
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0002 - val_loss: 1.1492
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9897 - val_loss: 1.1444
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0117 - val_loss: 1.1308
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9850 - val_loss: 1.1240
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9876 - val_loss: 1.1102
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9978 - val_loss: 1.1266
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9876 - val_loss: 1.1466
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9809 - val_loss: 1.1493
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9942 - val_loss: 1.1241
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0010 - val_loss: 1.1268
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9849 - val_loss: 1.1146
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9883 - val_loss: 1.1180
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9890 - val_loss: 1.1204
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9867 - val_loss: 1.1288
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.27 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.29 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.37 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.22 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 8.17 seconds.
Calculated PHATE in 10.07 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_144 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_145 (Dense)               (None, 32)           2080        dense_144[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_145[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_145[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_146 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_147 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_148 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_144 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_145 (Dense)               (None, 32)           2080        dense_144[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_145[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_145[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_480 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_480 ( [()]                 0           tf_op_layer_Shape_480[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_48 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_480[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_48[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_144 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_48 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_144[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_481 (TensorFl [(2,)]               0           tf_op_layer_Add_48[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_483 (TensorFl [(2,)]               0           tf_op_layer_Add_48[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_482 (TensorFl [(2,)]               0           tf_op_layer_Add_48[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_484 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_486 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_485 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_487 (TensorFl [(2,)]               0           tf_op_layer_Add_48[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_489 (TensorFl [(2,)]               0           tf_op_layer_Add_48[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_488 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_481 ( [()]                 0           tf_op_layer_Shape_481[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_483 ( [()]                 0           tf_op_layer_Shape_483[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_482 ( [()]                 0           tf_op_layer_Shape_482[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_484 ( [()]                 0           tf_op_layer_Shape_484[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_486 ( [()]                 0           tf_op_layer_Shape_486[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_485 ( [()]                 0           tf_op_layer_Shape_485[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_487 ( [()]                 0           tf_op_layer_Shape_487[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_489 ( [()]                 0           tf_op_layer_Shape_489[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_488 ( [()]                 0           tf_op_layer_Shape_488[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_288/shape ( [(3,)]               0           tf_op_layer_strided_slice_481[0][
                                                                 tf_op_layer_strided_slice_483[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_289/shape ( [(3,)]               0           tf_op_layer_strided_slice_482[0][
                                                                 tf_op_layer_strided_slice_483[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_290/shape ( [(3,)]               0           tf_op_layer_strided_slice_484[0][
                                                                 tf_op_layer_strided_slice_486[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_291/shape ( [(3,)]               0           tf_op_layer_strided_slice_485[0][
                                                                 tf_op_layer_strided_slice_486[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_292/shape ( [(3,)]               0           tf_op_layer_strided_slice_487[0][
                                                                 tf_op_layer_strided_slice_489[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_293/shape ( [(3,)]               0           tf_op_layer_strided_slice_488[0][
                                                                 tf_op_layer_strided_slice_489[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_288 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_48[0][0]         
                                                                 tf_op_layer_Reshape_288/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_288/multiples  [(3,)]               0           tf_op_layer_strided_slice_482[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_289 (Tensor [(1, None, None)]    0           tf_op_layer_Add_48[0][0]         
                                                                 tf_op_layer_Reshape_289/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_289/multiples  [(3,)]               0           tf_op_layer_strided_slice_481[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_290 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_290/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_290/multiples  [(3,)]               0           tf_op_layer_strided_slice_485[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_291 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_291/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_291/multiples  [(3,)]               0           tf_op_layer_strided_slice_484[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_292 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_48[0][0]         
                                                                 tf_op_layer_Reshape_292/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_292/multiples  [(3,)]               0           tf_op_layer_strided_slice_488[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_293 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_293/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_293/multiples  [(3,)]               0           tf_op_layer_strided_slice_487[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_288 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_288[0][0]    
                                                                 tf_op_layer_Tile_288/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_289 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_289[0][0]    
                                                                 tf_op_layer_Tile_289/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_290 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_290[0][0]    
                                                                 tf_op_layer_Tile_290/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_291 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_291[0][0]    
                                                                 tf_op_layer_Tile_291/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_292 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_292[0][0]    
                                                                 tf_op_layer_Tile_292/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_293 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_293[0][0]    
                                                                 tf_op_layer_Tile_293/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_240 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_288[0][0]       
                                                                 tf_op_layer_Tile_289[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_241 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_290[0][0]       
                                                                 tf_op_layer_Tile_291[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_242 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_292[0][0]       
                                                                 tf_op_layer_Tile_293[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_192 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_240[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_193 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_241[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_194 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_242[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_336 (TensorFlo [(None, None)]       0           tf_op_layer_Square_192[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_337 (TensorFlo [(None, None)]       0           tf_op_layer_Square_193[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_338 (TensorFlo [(None, None)]       0           tf_op_layer_Square_194[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_144 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_336[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_144 (TensorFlo [()]                 0           tf_op_layer_strided_slice_483[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_145 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_337[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_145 (TensorFlo [()]                 0           tf_op_layer_strided_slice_486[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_146 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_338[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_146 (TensorFlo [()]                 0           tf_op_layer_strided_slice_489[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_144 (Tensor [(None, None)]       0           tf_op_layer_Neg_144[0][0]        
                                                                 tf_op_layer_Cast_144[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_145 (Tensor [(None, None)]       0           tf_op_layer_Neg_145[0][0]        
                                                                 tf_op_layer_Cast_145[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_146 (Tensor [(None, None)]       0           tf_op_layer_Neg_146[0][0]        
                                                                 tf_op_layer_Cast_146[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_144 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_144[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_145 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_145[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_146 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_146[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_339 (TensorFlo [()]                 0           tf_op_layer_Exp_144[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_340 (TensorFlo [()]                 0           tf_op_layer_Exp_145[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_341 (TensorFlo [()]                 0           tf_op_layer_Exp_146[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_244 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_96 (TensorFlo [()]                 0           tf_op_layer_Mean_339[0][0]       
                                                                 tf_op_layer_Mean_340[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_145 (TensorFlow [()]                 0           tf_op_layer_Mean_341[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_195 (TensorF [(None, 97)]         0           tf_op_layer_Sub_244[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_243 (TensorFlow [()]                 0           tf_op_layer_AddV2_96[0][0]       
                                                                 tf_op_layer_Mul_145[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_48 (TensorFlowO [(None,)]            0           tf_op_layer_Square_195[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_146 (TensorFlow [()]                 0           tf_op_layer_Sub_243[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_97 (TensorFlo [(None,)]            0           tf_op_layer_Sum_48[0][0]         
                                                                 tf_op_layer_Mul_146[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_342 (TensorFlo [()]                 0           tf_op_layer_AddV2_97[0][0]       
__________________________________________________________________________________________________
add_loss_48 (AddLoss)           ()                   0           tf_op_layer_Mean_342[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.2103WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.1762s). Check your callbacks.
24/24 [==============================] - 1s 22ms/step - loss: 4.5566 - val_loss: 1.8587
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5995 - val_loss: 1.2635
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2794 - val_loss: 1.2296
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2134 - val_loss: 1.1340
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1660 - val_loss: 1.0765
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1276 - val_loss: 1.0933
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1018 - val_loss: 1.0443
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0909 - val_loss: 1.0327
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0346 - val_loss: 0.9824
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9667 - val_loss: 0.9157
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8814 - val_loss: 0.8253
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8532 - val_loss: 0.8224
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8414 - val_loss: 0.8005
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8291 - val_loss: 0.7937
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8279 - val_loss: 0.7962
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8268 - val_loss: 0.7840
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8091 - val_loss: 0.7903
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7951 - val_loss: 0.7965
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7952 - val_loss: 0.7783
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7896 - val_loss: 0.7544
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7853 - val_loss: 0.7990
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7881 - val_loss: 0.7495
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7753 - val_loss: 0.7548
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7791 - val_loss: 0.7611
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7760 - val_loss: 0.7539
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7627 - val_loss: 0.7446
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7694 - val_loss: 0.7370
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7679 - val_loss: 0.7395
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7663 - val_loss: 0.7562
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7609 - val_loss: 0.7388
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7648 - val_loss: 0.7366
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7522 - val_loss: 0.7303
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7568 - val_loss: 0.7356
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7432 - val_loss: 0.7869
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7508 - val_loss: 0.7312
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7443 - val_loss: 0.7439
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7545 - val_loss: 0.7254
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7411 - val_loss: 0.7263
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7479 - val_loss: 0.7272
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7346 - val_loss: 0.7315
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7416 - val_loss: 0.7309
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7451 - val_loss: 0.7409
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7482 - val_loss: 0.7335
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7318 - val_loss: 0.7371
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7338 - val_loss: 0.7213
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7294 - val_loss: 0.7290
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7337 - val_loss: 0.7279
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7300 - val_loss: 0.7246
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7295 - val_loss: 0.7352
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7411 - val_loss: 0.7204
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7287 - val_loss: 0.7375
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7290 - val_loss: 0.7283
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7365 - val_loss: 0.7339
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7381 - val_loss: 0.7254
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7164 - val_loss: 0.7129
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7244 - val_loss: 0.7289
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7239 - val_loss: 0.7287
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7207 - val_loss: 0.7228
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7176 - val_loss: 0.7105
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7169 - val_loss: 0.7416
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7295 - val_loss: 0.7092
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7111 - val_loss: 0.7130
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7160 - val_loss: 0.7147
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7127 - val_loss: 0.7242
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7173 - val_loss: 0.7356
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7151 - val_loss: 0.7197
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7102 - val_loss: 0.7228
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7035 - val_loss: 0.7048
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7057 - val_loss: 0.7385
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7132 - val_loss: 0.7266
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7070 - val_loss: 0.7216
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7060 - val_loss: 0.7142
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6975 - val_loss: 0.7089
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7006 - val_loss: 0.7030
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7032 - val_loss: 0.7045
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7027 - val_loss: 0.7136
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7010 - val_loss: 0.7073
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6957 - val_loss: 0.7663
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6937 - val_loss: 0.7332
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7025 - val_loss: 0.7266
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7036 - val_loss: 0.7120
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7042 - val_loss: 0.7267
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6940 - val_loss: 0.7336
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6933 - val_loss: 0.7157
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6996 - val_loss: 0.7046
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6921 - val_loss: 0.7065
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6872 - val_loss: 0.7093
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6991 - val_loss: 0.7236
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6927 - val_loss: 0.7222
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6912 - val_loss: 0.7138
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6935 - val_loss: 0.7005
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6883 - val_loss: 0.7186
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6940 - val_loss: 0.7025
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6914 - val_loss: 0.7014
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6867 - val_loss: 0.7168
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6872 - val_loss: 0.7692
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6951 - val_loss: 0.7123
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6828 - val_loss: 0.7297
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6865 - val_loss: 0.7001
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6893 - val_loss: 0.7027
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_149 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_490 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_490 ( [()]                 0           tf_op_layer_Shape_490[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_49 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_490[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_49[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_147 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_49 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_147[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_491 (TensorFl [(2,)]               0           tf_op_layer_Add_49[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_493 (TensorFl [(2,)]               0           tf_op_layer_Add_49[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_492 (TensorFl [(2,)]               0           tf_op_layer_Add_49[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_494 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_496 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_495 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_497 (TensorFl [(2,)]               0           tf_op_layer_Add_49[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_499 (TensorFl [(2,)]               0           tf_op_layer_Add_49[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_498 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_491 ( [()]                 0           tf_op_layer_Shape_491[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_493 ( [()]                 0           tf_op_layer_Shape_493[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_492 ( [()]                 0           tf_op_layer_Shape_492[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_494 ( [()]                 0           tf_op_layer_Shape_494[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_496 ( [()]                 0           tf_op_layer_Shape_496[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_495 ( [()]                 0           tf_op_layer_Shape_495[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_497 ( [()]                 0           tf_op_layer_Shape_497[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_499 ( [()]                 0           tf_op_layer_Shape_499[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_498 ( [()]                 0           tf_op_layer_Shape_498[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_294/shape ( [(3,)]               0           tf_op_layer_strided_slice_491[0][
                                                                 tf_op_layer_strided_slice_493[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_295/shape ( [(3,)]               0           tf_op_layer_strided_slice_492[0][
                                                                 tf_op_layer_strided_slice_493[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_296/shape ( [(3,)]               0           tf_op_layer_strided_slice_494[0][
                                                                 tf_op_layer_strided_slice_496[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_297/shape ( [(3,)]               0           tf_op_layer_strided_slice_495[0][
                                                                 tf_op_layer_strided_slice_496[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_298/shape ( [(3,)]               0           tf_op_layer_strided_slice_497[0][
                                                                 tf_op_layer_strided_slice_499[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_299/shape ( [(3,)]               0           tf_op_layer_strided_slice_498[0][
                                                                 tf_op_layer_strided_slice_499[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_294 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_49[0][0]         
                                                                 tf_op_layer_Reshape_294/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_294/multiples  [(3,)]               0           tf_op_layer_strided_slice_492[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_295 (Tensor [(1, None, None)]    0           tf_op_layer_Add_49[0][0]         
                                                                 tf_op_layer_Reshape_295/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_295/multiples  [(3,)]               0           tf_op_layer_strided_slice_491[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_296 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_296/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_296/multiples  [(3,)]               0           tf_op_layer_strided_slice_495[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_297 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_297/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_297/multiples  [(3,)]               0           tf_op_layer_strided_slice_494[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_298 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_49[0][0]         
                                                                 tf_op_layer_Reshape_298/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_298/multiples  [(3,)]               0           tf_op_layer_strided_slice_498[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_299 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_299/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_299/multiples  [(3,)]               0           tf_op_layer_strided_slice_497[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_294 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_294[0][0]    
                                                                 tf_op_layer_Tile_294/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_295 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_295[0][0]    
                                                                 tf_op_layer_Tile_295/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_296 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_296[0][0]    
                                                                 tf_op_layer_Tile_296/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_297 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_297[0][0]    
                                                                 tf_op_layer_Tile_297/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_298 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_298[0][0]    
                                                                 tf_op_layer_Tile_298/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_299 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_299[0][0]    
                                                                 tf_op_layer_Tile_299/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_245 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_294[0][0]       
                                                                 tf_op_layer_Tile_295[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_246 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_296[0][0]       
                                                                 tf_op_layer_Tile_297[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_247 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_298[0][0]       
                                                                 tf_op_layer_Tile_299[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_196 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_245[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_197 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_246[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_198 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_247[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_343 (TensorFlo [(None, None)]       0           tf_op_layer_Square_196[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_344 (TensorFlo [(None, None)]       0           tf_op_layer_Square_197[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_345 (TensorFlo [(None, None)]       0           tf_op_layer_Square_198[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_147 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_343[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_147 (TensorFlo [()]                 0           tf_op_layer_strided_slice_493[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_148 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_344[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_148 (TensorFlo [()]                 0           tf_op_layer_strided_slice_496[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_149 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_345[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_149 (TensorFlo [()]                 0           tf_op_layer_strided_slice_499[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_147 (Tensor [(None, None)]       0           tf_op_layer_Neg_147[0][0]        
                                                                 tf_op_layer_Cast_147[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_148 (Tensor [(None, None)]       0           tf_op_layer_Neg_148[0][0]        
                                                                 tf_op_layer_Cast_148[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_149 (Tensor [(None, None)]       0           tf_op_layer_Neg_149[0][0]        
                                                                 tf_op_layer_Cast_149[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_147 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_147[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_148 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_148[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_149 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_149[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_346 (TensorFlo [()]                 0           tf_op_layer_Exp_147[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_347 (TensorFlo [()]                 0           tf_op_layer_Exp_148[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_348 (TensorFlo [()]                 0           tf_op_layer_Exp_149[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_249 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_98 (TensorFlo [()]                 0           tf_op_layer_Mean_346[0][0]       
                                                                 tf_op_layer_Mean_347[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_148 (TensorFlow [()]                 0           tf_op_layer_Mean_348[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_199 (TensorF [(None, 97)]         0           tf_op_layer_Sub_249[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_248 (TensorFlow [()]                 0           tf_op_layer_AddV2_98[0][0]       
                                                                 tf_op_layer_Mul_148[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_49 (TensorFlowO [(None,)]            0           tf_op_layer_Square_199[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_149 (TensorFlow [()]                 0           tf_op_layer_Sub_248[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_99 (TensorFlo [(None,)]            0           tf_op_layer_Sum_49[0][0]         
                                                                 tf_op_layer_Mul_149[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_349 (TensorFlo [()]                 0           tf_op_layer_AddV2_99[0][0]       
__________________________________________________________________________________________________
add_loss_49 (AddLoss)           ()                   0           tf_op_layer_Mean_349[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.7963WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1447s). Check your callbacks.
24/24 [==============================] - 0s 20ms/step - loss: 8.1445 - val_loss: 5.6132
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 4.2580 - val_loss: 3.4108
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.5575 - val_loss: 2.4622
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.0551 - val_loss: 2.1237
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8649 - val_loss: 1.9631
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7196 - val_loss: 1.8033
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6092 - val_loss: 1.6564
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5253 - val_loss: 1.5840
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4397 - val_loss: 1.5159
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3852 - val_loss: 1.4051
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3226 - val_loss: 1.3435
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3006 - val_loss: 1.3508
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2433 - val_loss: 1.2830
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2121 - val_loss: 1.2809
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2121 - val_loss: 1.2712
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1965 - val_loss: 1.2567
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1696 - val_loss: 1.2454
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1737 - val_loss: 1.2355
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1560 - val_loss: 1.1994
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1595 - val_loss: 1.2416
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1463 - val_loss: 1.2111
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1339 - val_loss: 1.1886
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1281 - val_loss: 1.1705
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1249 - val_loss: 1.1860
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1149 - val_loss: 1.1737
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1074 - val_loss: 1.1819
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1049 - val_loss: 1.1346
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0972 - val_loss: 1.1612
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0941 - val_loss: 1.1699
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0913 - val_loss: 1.1430
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0931 - val_loss: 1.1469
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0838 - val_loss: 1.1461
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0733 - val_loss: 1.1463
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0689 - val_loss: 1.1447
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0721 - val_loss: 1.1613
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0767 - val_loss: 1.1352
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0736 - val_loss: 1.1156
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0570 - val_loss: 1.1223
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0550 - val_loss: 1.1313
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0583 - val_loss: 1.1286
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0614 - val_loss: 1.1359
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0533 - val_loss: 1.1606
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0483 - val_loss: 1.1115
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0464 - val_loss: 1.1238
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0472 - val_loss: 1.1069
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0448 - val_loss: 1.0895
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0438 - val_loss: 1.0995
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0311 - val_loss: 1.1114
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0396 - val_loss: 1.0944
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0310 - val_loss: 1.0952
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0405 - val_loss: 1.0969
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0347 - val_loss: 1.1079
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0442 - val_loss: 1.0947
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0408 - val_loss: 1.0888
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0291 - val_loss: 1.0849
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0296 - val_loss: 1.1043
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0164 - val_loss: 1.0934
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0328 - val_loss: 1.1218
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0197 - val_loss: 1.1065
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0314 - val_loss: 1.0821
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0304 - val_loss: 1.0944
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0171 - val_loss: 1.1028
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0278 - val_loss: 1.1240
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0240 - val_loss: 1.1044
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0194 - val_loss: 1.1123
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0125 - val_loss: 1.0855
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0312 - val_loss: 1.0599
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0101 - val_loss: 1.0921
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0214 - val_loss: 1.0822
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0219 - val_loss: 1.0612
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0190 - val_loss: 1.0879
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0189 - val_loss: 1.0890
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0212 - val_loss: 1.0721
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0061 - val_loss: 1.0515
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0053 - val_loss: 1.0552
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0035 - val_loss: 1.0890
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9971 - val_loss: 1.0894
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0066 - val_loss: 1.0828
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0029 - val_loss: 1.0681
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9969 - val_loss: 1.0950
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0102 - val_loss: 1.0943
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9984 - val_loss: 1.0762
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0037 - val_loss: 1.0852
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0058 - val_loss: 1.0931
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9988 - val_loss: 1.0772
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0011 - val_loss: 1.0551
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0069 - val_loss: 1.0655
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9987 - val_loss: 1.0739
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9784 - val_loss: 1.0923
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9886 - val_loss: 1.0465
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9832 - val_loss: 1.0484
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0043 - val_loss: 1.0613
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0016 - val_loss: 1.0674
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9902 - val_loss: 1.0783
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0019 - val_loss: 1.0424
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0059 - val_loss: 1.0536
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0022 - val_loss: 1.0808
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9922 - val_loss: 1.0455
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9916 - val_loss: 1.0648
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9897 - val_loss: 1.0732
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.30 seconds.
    Calculating affinities...
    Calculated affinities in 0.02 seconds.
  Calculated graph and diffusion operator in 0.33 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.61 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.23 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 7.14 seconds.
Calculated PHATE in 9.32 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_150 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_151 (Dense)               (None, 32)           2080        dense_150[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_151[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_151[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_152 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_153 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_154 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_150 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_151 (Dense)               (None, 32)           2080        dense_150[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_151[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_151[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_500 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_500 ( [()]                 0           tf_op_layer_Shape_500[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_50 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_500[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_50[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_150 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_50 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_150[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_501 (TensorFl [(2,)]               0           tf_op_layer_Add_50[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_503 (TensorFl [(2,)]               0           tf_op_layer_Add_50[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_502 (TensorFl [(2,)]               0           tf_op_layer_Add_50[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_504 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_506 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_505 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_507 (TensorFl [(2,)]               0           tf_op_layer_Add_50[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_509 (TensorFl [(2,)]               0           tf_op_layer_Add_50[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_508 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_501 ( [()]                 0           tf_op_layer_Shape_501[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_503 ( [()]                 0           tf_op_layer_Shape_503[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_502 ( [()]                 0           tf_op_layer_Shape_502[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_504 ( [()]                 0           tf_op_layer_Shape_504[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_506 ( [()]                 0           tf_op_layer_Shape_506[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_505 ( [()]                 0           tf_op_layer_Shape_505[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_507 ( [()]                 0           tf_op_layer_Shape_507[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_509 ( [()]                 0           tf_op_layer_Shape_509[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_508 ( [()]                 0           tf_op_layer_Shape_508[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_300/shape ( [(3,)]               0           tf_op_layer_strided_slice_501[0][
                                                                 tf_op_layer_strided_slice_503[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_301/shape ( [(3,)]               0           tf_op_layer_strided_slice_502[0][
                                                                 tf_op_layer_strided_slice_503[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_302/shape ( [(3,)]               0           tf_op_layer_strided_slice_504[0][
                                                                 tf_op_layer_strided_slice_506[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_303/shape ( [(3,)]               0           tf_op_layer_strided_slice_505[0][
                                                                 tf_op_layer_strided_slice_506[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_304/shape ( [(3,)]               0           tf_op_layer_strided_slice_507[0][
                                                                 tf_op_layer_strided_slice_509[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_305/shape ( [(3,)]               0           tf_op_layer_strided_slice_508[0][
                                                                 tf_op_layer_strided_slice_509[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_300 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_50[0][0]         
                                                                 tf_op_layer_Reshape_300/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_300/multiples  [(3,)]               0           tf_op_layer_strided_slice_502[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_301 (Tensor [(1, None, None)]    0           tf_op_layer_Add_50[0][0]         
                                                                 tf_op_layer_Reshape_301/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_301/multiples  [(3,)]               0           tf_op_layer_strided_slice_501[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_302 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_302/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_302/multiples  [(3,)]               0           tf_op_layer_strided_slice_505[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_303 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_303/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_303/multiples  [(3,)]               0           tf_op_layer_strided_slice_504[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_304 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_50[0][0]         
                                                                 tf_op_layer_Reshape_304/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_304/multiples  [(3,)]               0           tf_op_layer_strided_slice_508[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_305 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_305/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_305/multiples  [(3,)]               0           tf_op_layer_strided_slice_507[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_300 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_300[0][0]    
                                                                 tf_op_layer_Tile_300/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_301 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_301[0][0]    
                                                                 tf_op_layer_Tile_301/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_302 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_302[0][0]    
                                                                 tf_op_layer_Tile_302/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_303 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_303[0][0]    
                                                                 tf_op_layer_Tile_303/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_304 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_304[0][0]    
                                                                 tf_op_layer_Tile_304/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_305 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_305[0][0]    
                                                                 tf_op_layer_Tile_305/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_250 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_300[0][0]       
                                                                 tf_op_layer_Tile_301[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_251 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_302[0][0]       
                                                                 tf_op_layer_Tile_303[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_252 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_304[0][0]       
                                                                 tf_op_layer_Tile_305[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_200 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_250[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_201 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_251[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_202 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_252[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_350 (TensorFlo [(None, None)]       0           tf_op_layer_Square_200[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_351 (TensorFlo [(None, None)]       0           tf_op_layer_Square_201[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_352 (TensorFlo [(None, None)]       0           tf_op_layer_Square_202[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_150 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_350[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_150 (TensorFlo [()]                 0           tf_op_layer_strided_slice_503[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_151 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_351[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_151 (TensorFlo [()]                 0           tf_op_layer_strided_slice_506[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_152 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_352[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_152 (TensorFlo [()]                 0           tf_op_layer_strided_slice_509[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_150 (Tensor [(None, None)]       0           tf_op_layer_Neg_150[0][0]        
                                                                 tf_op_layer_Cast_150[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_151 (Tensor [(None, None)]       0           tf_op_layer_Neg_151[0][0]        
                                                                 tf_op_layer_Cast_151[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_152 (Tensor [(None, None)]       0           tf_op_layer_Neg_152[0][0]        
                                                                 tf_op_layer_Cast_152[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_150 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_150[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_151 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_151[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_152 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_152[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_353 (TensorFlo [()]                 0           tf_op_layer_Exp_150[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_354 (TensorFlo [()]                 0           tf_op_layer_Exp_151[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_355 (TensorFlo [()]                 0           tf_op_layer_Exp_152[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_254 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_100 (TensorFl [()]                 0           tf_op_layer_Mean_353[0][0]       
                                                                 tf_op_layer_Mean_354[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_151 (TensorFlow [()]                 0           tf_op_layer_Mean_355[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_203 (TensorF [(None, 97)]         0           tf_op_layer_Sub_254[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_253 (TensorFlow [()]                 0           tf_op_layer_AddV2_100[0][0]      
                                                                 tf_op_layer_Mul_151[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_50 (TensorFlowO [(None,)]            0           tf_op_layer_Square_203[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_152 (TensorFlow [()]                 0           tf_op_layer_Sub_253[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_101 (TensorFl [(None,)]            0           tf_op_layer_Sum_50[0][0]         
                                                                 tf_op_layer_Mul_152[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_356 (TensorFlo [()]                 0           tf_op_layer_AddV2_101[0][0]      
__________________________________________________________________________________________________
add_loss_50 (AddLoss)           ()                   0           tf_op_layer_Mean_356[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.8486 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.1363s). Check your callbacks.
24/24 [==============================] - 1s 22ms/step - loss: 4.6092 - val_loss: 2.0441
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5917 - val_loss: 1.6109
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3512 - val_loss: 1.3402
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2047 - val_loss: 1.3443
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1419 - val_loss: 1.2576
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1001 - val_loss: 1.1491
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0023 - val_loss: 1.0601
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8902 - val_loss: 0.9691
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8627 - val_loss: 0.9761
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8553 - val_loss: 0.9227
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8380 - val_loss: 0.9001
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8267 - val_loss: 0.9125
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8154 - val_loss: 0.8702
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8224 - val_loss: 0.9106
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8053 - val_loss: 0.8657
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8006 - val_loss: 0.8695
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7904 - val_loss: 0.8624
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7921 - val_loss: 0.8733
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7960 - val_loss: 0.8358
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7986 - val_loss: 0.8871
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7912 - val_loss: 0.8726
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7779 - val_loss: 0.8378
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7809 - val_loss: 0.8410
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7760 - val_loss: 0.8163
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7702 - val_loss: 0.8372
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7663 - val_loss: 0.8505
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7631 - val_loss: 0.8254
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7684 - val_loss: 0.8416
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7618 - val_loss: 0.8081
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7601 - val_loss: 0.8121
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7694 - val_loss: 0.8229
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7574 - val_loss: 0.8132
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7517 - val_loss: 0.7973
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7618 - val_loss: 0.8080
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7507 - val_loss: 0.7926
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7449 - val_loss: 0.8176
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7487 - val_loss: 0.8120
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7478 - val_loss: 0.8105
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7492 - val_loss: 0.8044
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7537 - val_loss: 0.7989
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7383 - val_loss: 0.8273
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7454 - val_loss: 0.8296
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7544 - val_loss: 0.7904
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7442 - val_loss: 0.8068
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7517 - val_loss: 0.7947
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7369 - val_loss: 0.7916
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7378 - val_loss: 0.7822
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7373 - val_loss: 0.8029
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7363 - val_loss: 0.7888
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7369 - val_loss: 0.7934
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7294 - val_loss: 0.8051
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7384 - val_loss: 0.7991
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7259 - val_loss: 0.7740
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7294 - val_loss: 0.7823
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7541 - val_loss: 0.7629
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7451 - val_loss: 0.7801
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7319 - val_loss: 0.8035
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7278 - val_loss: 0.7692
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7326 - val_loss: 0.7792
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7333 - val_loss: 0.7624
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7251 - val_loss: 0.7677
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7246 - val_loss: 0.7767
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7253 - val_loss: 0.8154
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7218 - val_loss: 0.7820
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7214 - val_loss: 0.7824
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7276 - val_loss: 0.7720
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7308 - val_loss: 0.7774
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7235 - val_loss: 0.7562
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7239 - val_loss: 0.7501
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7208 - val_loss: 0.7650
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7226 - val_loss: 0.7630
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7284 - val_loss: 0.7515
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7199 - val_loss: 0.7675
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7388 - val_loss: 0.7750
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7207 - val_loss: 0.7959
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7257 - val_loss: 0.7711
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7354 - val_loss: 0.7571
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7247 - val_loss: 0.7408
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7189 - val_loss: 0.7614
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7130 - val_loss: 0.7724
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7247 - val_loss: 0.7579
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7166 - val_loss: 0.7847
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7272 - val_loss: 0.7491
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7153 - val_loss: 0.7551
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7250 - val_loss: 0.7736
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7154 - val_loss: 0.7679
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7170 - val_loss: 0.7456
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7184 - val_loss: 0.7437
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7121 - val_loss: 0.7560
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7299 - val_loss: 0.7665
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7257 - val_loss: 0.7526
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7097 - val_loss: 0.7521
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7171 - val_loss: 0.7616
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7152 - val_loss: 0.7544
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7148 - val_loss: 0.7354
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7041 - val_loss: 0.7586
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7085 - val_loss: 0.7422
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7126 - val_loss: 0.7624
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7159 - val_loss: 0.8064
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7111 - val_loss: 0.7350
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_155 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_510 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_510 ( [()]                 0           tf_op_layer_Shape_510[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_51 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_510[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_51[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_153 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_51 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_153[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_511 (TensorFl [(2,)]               0           tf_op_layer_Add_51[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_513 (TensorFl [(2,)]               0           tf_op_layer_Add_51[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_512 (TensorFl [(2,)]               0           tf_op_layer_Add_51[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_514 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_516 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_515 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_517 (TensorFl [(2,)]               0           tf_op_layer_Add_51[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_519 (TensorFl [(2,)]               0           tf_op_layer_Add_51[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_518 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_511 ( [()]                 0           tf_op_layer_Shape_511[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_513 ( [()]                 0           tf_op_layer_Shape_513[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_512 ( [()]                 0           tf_op_layer_Shape_512[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_514 ( [()]                 0           tf_op_layer_Shape_514[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_516 ( [()]                 0           tf_op_layer_Shape_516[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_515 ( [()]                 0           tf_op_layer_Shape_515[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_517 ( [()]                 0           tf_op_layer_Shape_517[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_519 ( [()]                 0           tf_op_layer_Shape_519[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_518 ( [()]                 0           tf_op_layer_Shape_518[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_306/shape ( [(3,)]               0           tf_op_layer_strided_slice_511[0][
                                                                 tf_op_layer_strided_slice_513[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_307/shape ( [(3,)]               0           tf_op_layer_strided_slice_512[0][
                                                                 tf_op_layer_strided_slice_513[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_308/shape ( [(3,)]               0           tf_op_layer_strided_slice_514[0][
                                                                 tf_op_layer_strided_slice_516[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_309/shape ( [(3,)]               0           tf_op_layer_strided_slice_515[0][
                                                                 tf_op_layer_strided_slice_516[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_310/shape ( [(3,)]               0           tf_op_layer_strided_slice_517[0][
                                                                 tf_op_layer_strided_slice_519[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_311/shape ( [(3,)]               0           tf_op_layer_strided_slice_518[0][
                                                                 tf_op_layer_strided_slice_519[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_306 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_51[0][0]         
                                                                 tf_op_layer_Reshape_306/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_306/multiples  [(3,)]               0           tf_op_layer_strided_slice_512[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_307 (Tensor [(1, None, None)]    0           tf_op_layer_Add_51[0][0]         
                                                                 tf_op_layer_Reshape_307/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_307/multiples  [(3,)]               0           tf_op_layer_strided_slice_511[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_308 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_308/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_308/multiples  [(3,)]               0           tf_op_layer_strided_slice_515[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_309 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_309/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_309/multiples  [(3,)]               0           tf_op_layer_strided_slice_514[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_310 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_51[0][0]         
                                                                 tf_op_layer_Reshape_310/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_310/multiples  [(3,)]               0           tf_op_layer_strided_slice_518[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_311 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_311/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_311/multiples  [(3,)]               0           tf_op_layer_strided_slice_517[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_306 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_306[0][0]    
                                                                 tf_op_layer_Tile_306/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_307 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_307[0][0]    
                                                                 tf_op_layer_Tile_307/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_308 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_308[0][0]    
                                                                 tf_op_layer_Tile_308/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_309 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_309[0][0]    
                                                                 tf_op_layer_Tile_309/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_310 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_310[0][0]    
                                                                 tf_op_layer_Tile_310/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_311 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_311[0][0]    
                                                                 tf_op_layer_Tile_311/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_255 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_306[0][0]       
                                                                 tf_op_layer_Tile_307[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_256 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_308[0][0]       
                                                                 tf_op_layer_Tile_309[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_257 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_310[0][0]       
                                                                 tf_op_layer_Tile_311[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_204 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_255[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_205 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_256[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_206 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_257[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_357 (TensorFlo [(None, None)]       0           tf_op_layer_Square_204[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_358 (TensorFlo [(None, None)]       0           tf_op_layer_Square_205[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_359 (TensorFlo [(None, None)]       0           tf_op_layer_Square_206[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_153 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_357[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_153 (TensorFlo [()]                 0           tf_op_layer_strided_slice_513[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_154 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_358[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_154 (TensorFlo [()]                 0           tf_op_layer_strided_slice_516[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_155 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_359[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_155 (TensorFlo [()]                 0           tf_op_layer_strided_slice_519[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_153 (Tensor [(None, None)]       0           tf_op_layer_Neg_153[0][0]        
                                                                 tf_op_layer_Cast_153[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_154 (Tensor [(None, None)]       0           tf_op_layer_Neg_154[0][0]        
                                                                 tf_op_layer_Cast_154[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_155 (Tensor [(None, None)]       0           tf_op_layer_Neg_155[0][0]        
                                                                 tf_op_layer_Cast_155[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_153 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_153[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_154 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_154[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_155 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_155[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_360 (TensorFlo [()]                 0           tf_op_layer_Exp_153[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_361 (TensorFlo [()]                 0           tf_op_layer_Exp_154[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_362 (TensorFlo [()]                 0           tf_op_layer_Exp_155[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_259 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_102 (TensorFl [()]                 0           tf_op_layer_Mean_360[0][0]       
                                                                 tf_op_layer_Mean_361[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_154 (TensorFlow [()]                 0           tf_op_layer_Mean_362[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_207 (TensorF [(None, 97)]         0           tf_op_layer_Sub_259[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_258 (TensorFlow [()]                 0           tf_op_layer_AddV2_102[0][0]      
                                                                 tf_op_layer_Mul_154[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_51 (TensorFlowO [(None,)]            0           tf_op_layer_Square_207[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_155 (TensorFlow [()]                 0           tf_op_layer_Sub_258[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_103 (TensorFl [(None,)]            0           tf_op_layer_Sum_51[0][0]         
                                                                 tf_op_layer_Mul_155[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_363 (TensorFlo [()]                 0           tf_op_layer_AddV2_103[0][0]      
__________________________________________________________________________________________________
add_loss_51 (AddLoss)           ()                   0           tf_op_layer_Mean_363[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.4687WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.1292s). Check your callbacks.
24/24 [==============================] - 0s 15ms/step - loss: 7.6363 - val_loss: 5.6970
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 3.9420 - val_loss: 3.3339
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.4361 - val_loss: 2.5668
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.9810 - val_loss: 2.1848
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7673 - val_loss: 2.0942
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6290 - val_loss: 1.8650
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5618 - val_loss: 1.7994
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4279 - val_loss: 1.6500
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3527 - val_loss: 1.5805
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2840 - val_loss: 1.5477
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2411 - val_loss: 1.4525
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2231 - val_loss: 1.4950
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1878 - val_loss: 1.4308
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1700 - val_loss: 1.3975
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1667 - val_loss: 1.4172
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1331 - val_loss: 1.3455
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1411 - val_loss: 1.3266
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1104 - val_loss: 1.2814
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1080 - val_loss: 1.2940
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1249 - val_loss: 1.2805
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0981 - val_loss: 1.3238
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0826 - val_loss: 1.3288
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0960 - val_loss: 1.3142
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0679 - val_loss: 1.3110
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0672 - val_loss: 1.2954
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0685 - val_loss: 1.2898
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0588 - val_loss: 1.3142
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0584 - val_loss: 1.2443
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0573 - val_loss: 1.3205
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0460 - val_loss: 1.2305
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0506 - val_loss: 1.2879
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0551 - val_loss: 1.2493
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0406 - val_loss: 1.2790
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0240 - val_loss: 1.2683
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0359 - val_loss: 1.2385
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0323 - val_loss: 1.2406
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0259 - val_loss: 1.2720
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0225 - val_loss: 1.2672
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0438 - val_loss: 1.2195
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0166 - val_loss: 1.2391
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0216 - val_loss: 1.2045
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0058 - val_loss: 1.1707
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0190 - val_loss: 1.1988
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0142 - val_loss: 1.1996
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0100 - val_loss: 1.1637
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0029 - val_loss: 1.2322
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0173 - val_loss: 1.2744
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0079 - val_loss: 1.1859
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0072 - val_loss: 1.2107
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9899 - val_loss: 1.2048
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9999 - val_loss: 1.2517
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9931 - val_loss: 1.1957
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9956 - val_loss: 1.2541
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9895 - val_loss: 1.2175
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9836 - val_loss: 1.1890
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9918 - val_loss: 1.2062
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9924 - val_loss: 1.1616
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9804 - val_loss: 1.2195
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9871 - val_loss: 1.2058
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9857 - val_loss: 1.1782
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9940 - val_loss: 1.1805
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0018 - val_loss: 1.1737
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9892 - val_loss: 1.2077
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9813 - val_loss: 1.1622
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9781 - val_loss: 1.1894
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9827 - val_loss: 1.1541
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9780 - val_loss: 1.1808
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9890 - val_loss: 1.1946
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9725 - val_loss: 1.2369
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9724 - val_loss: 1.2249
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9715 - val_loss: 1.1836
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9807 - val_loss: 1.2454
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9852 - val_loss: 1.2131
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9783 - val_loss: 1.1665
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9756 - val_loss: 1.1661
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9840 - val_loss: 1.1500
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9722 - val_loss: 1.2040
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9761 - val_loss: 1.2414
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9750 - val_loss: 1.1995
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9859 - val_loss: 1.1850
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9684 - val_loss: 1.2271
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9703 - val_loss: 1.1736
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9696 - val_loss: 1.1834
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9516 - val_loss: 1.1363
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9645 - val_loss: 1.1503
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9600 - val_loss: 1.2037
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9727 - val_loss: 1.1495
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9666 - val_loss: 1.1610
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9729 - val_loss: 1.1904
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9644 - val_loss: 1.2018
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9649 - val_loss: 1.2141
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9509 - val_loss: 1.1745
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9559 - val_loss: 1.2144
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9672 - val_loss: 1.1498
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9487 - val_loss: 1.1611
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9640 - val_loss: 1.1363
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9595 - val_loss: 1.1438
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9597 - val_loss: 1.1987
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9514 - val_loss: 1.1669
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9680 - val_loss: 1.1673
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.28 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.30 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.39 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.36 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 10.21 seconds.
Calculated PHATE in 12.28 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_156 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_157 (Dense)               (None, 32)           2080        dense_156[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_157[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_157[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_158 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_159 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_160 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_156 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_157 (Dense)               (None, 32)           2080        dense_156[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_157[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_157[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_520 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_520 ( [()]                 0           tf_op_layer_Shape_520[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_52 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_520[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_52[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_156 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_52 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_156[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_521 (TensorFl [(2,)]               0           tf_op_layer_Add_52[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_523 (TensorFl [(2,)]               0           tf_op_layer_Add_52[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_522 (TensorFl [(2,)]               0           tf_op_layer_Add_52[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_524 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_526 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_525 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_527 (TensorFl [(2,)]               0           tf_op_layer_Add_52[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_529 (TensorFl [(2,)]               0           tf_op_layer_Add_52[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_528 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_521 ( [()]                 0           tf_op_layer_Shape_521[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_523 ( [()]                 0           tf_op_layer_Shape_523[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_522 ( [()]                 0           tf_op_layer_Shape_522[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_524 ( [()]                 0           tf_op_layer_Shape_524[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_526 ( [()]                 0           tf_op_layer_Shape_526[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_525 ( [()]                 0           tf_op_layer_Shape_525[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_527 ( [()]                 0           tf_op_layer_Shape_527[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_529 ( [()]                 0           tf_op_layer_Shape_529[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_528 ( [()]                 0           tf_op_layer_Shape_528[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_312/shape ( [(3,)]               0           tf_op_layer_strided_slice_521[0][
                                                                 tf_op_layer_strided_slice_523[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_313/shape ( [(3,)]               0           tf_op_layer_strided_slice_522[0][
                                                                 tf_op_layer_strided_slice_523[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_314/shape ( [(3,)]               0           tf_op_layer_strided_slice_524[0][
                                                                 tf_op_layer_strided_slice_526[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_315/shape ( [(3,)]               0           tf_op_layer_strided_slice_525[0][
                                                                 tf_op_layer_strided_slice_526[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_316/shape ( [(3,)]               0           tf_op_layer_strided_slice_527[0][
                                                                 tf_op_layer_strided_slice_529[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_317/shape ( [(3,)]               0           tf_op_layer_strided_slice_528[0][
                                                                 tf_op_layer_strided_slice_529[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_312 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_52[0][0]         
                                                                 tf_op_layer_Reshape_312/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_312/multiples  [(3,)]               0           tf_op_layer_strided_slice_522[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_313 (Tensor [(1, None, None)]    0           tf_op_layer_Add_52[0][0]         
                                                                 tf_op_layer_Reshape_313/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_313/multiples  [(3,)]               0           tf_op_layer_strided_slice_521[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_314 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_314/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_314/multiples  [(3,)]               0           tf_op_layer_strided_slice_525[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_315 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_315/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_315/multiples  [(3,)]               0           tf_op_layer_strided_slice_524[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_316 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_52[0][0]         
                                                                 tf_op_layer_Reshape_316/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_316/multiples  [(3,)]               0           tf_op_layer_strided_slice_528[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_317 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_317/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_317/multiples  [(3,)]               0           tf_op_layer_strided_slice_527[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_312 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_312[0][0]    
                                                                 tf_op_layer_Tile_312/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_313 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_313[0][0]    
                                                                 tf_op_layer_Tile_313/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_314 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_314[0][0]    
                                                                 tf_op_layer_Tile_314/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_315 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_315[0][0]    
                                                                 tf_op_layer_Tile_315/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_316 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_316[0][0]    
                                                                 tf_op_layer_Tile_316/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_317 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_317[0][0]    
                                                                 tf_op_layer_Tile_317/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_260 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_312[0][0]       
                                                                 tf_op_layer_Tile_313[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_261 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_314[0][0]       
                                                                 tf_op_layer_Tile_315[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_262 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_316[0][0]       
                                                                 tf_op_layer_Tile_317[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_208 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_260[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_209 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_261[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_210 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_262[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_364 (TensorFlo [(None, None)]       0           tf_op_layer_Square_208[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_365 (TensorFlo [(None, None)]       0           tf_op_layer_Square_209[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_366 (TensorFlo [(None, None)]       0           tf_op_layer_Square_210[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_156 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_364[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_156 (TensorFlo [()]                 0           tf_op_layer_strided_slice_523[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_157 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_365[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_157 (TensorFlo [()]                 0           tf_op_layer_strided_slice_526[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_158 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_366[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_158 (TensorFlo [()]                 0           tf_op_layer_strided_slice_529[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_156 (Tensor [(None, None)]       0           tf_op_layer_Neg_156[0][0]        
                                                                 tf_op_layer_Cast_156[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_157 (Tensor [(None, None)]       0           tf_op_layer_Neg_157[0][0]        
                                                                 tf_op_layer_Cast_157[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_158 (Tensor [(None, None)]       0           tf_op_layer_Neg_158[0][0]        
                                                                 tf_op_layer_Cast_158[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_156 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_156[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_157 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_157[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_158 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_158[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_367 (TensorFlo [()]                 0           tf_op_layer_Exp_156[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_368 (TensorFlo [()]                 0           tf_op_layer_Exp_157[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_369 (TensorFlo [()]                 0           tf_op_layer_Exp_158[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_264 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_104 (TensorFl [()]                 0           tf_op_layer_Mean_367[0][0]       
                                                                 tf_op_layer_Mean_368[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_157 (TensorFlow [()]                 0           tf_op_layer_Mean_369[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_211 (TensorF [(None, 97)]         0           tf_op_layer_Sub_264[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_263 (TensorFlow [()]                 0           tf_op_layer_AddV2_104[0][0]      
                                                                 tf_op_layer_Mul_157[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_52 (TensorFlowO [(None,)]            0           tf_op_layer_Square_211[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_158 (TensorFlow [()]                 0           tf_op_layer_Sub_263[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_105 (TensorFl [(None,)]            0           tf_op_layer_Sum_52[0][0]         
                                                                 tf_op_layer_Mul_158[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_370 (TensorFlo [()]                 0           tf_op_layer_AddV2_105[0][0]      
__________________________________________________________________________________________________
add_loss_52 (AddLoss)           ()                   0           tf_op_layer_Mean_370[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.3884WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0021s vs `on_train_batch_end` time: 0.2396s). Check your callbacks.
24/24 [==============================] - 1s 33ms/step - loss: 4.5555 - val_loss: 1.7452
Epoch 2/100
24/24 [==============================] - 0s 4ms/step - loss: 1.5667 - val_loss: 1.2048
Epoch 3/100
24/24 [==============================] - 0s 5ms/step - loss: 1.2818 - val_loss: 1.1020
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2098 - val_loss: 1.0558
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1883 - val_loss: 1.0512
Epoch 6/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1542 - val_loss: 1.0205
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0895 - val_loss: 1.0243
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9876 - val_loss: 0.8492
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9062 - val_loss: 0.7905
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8858 - val_loss: 0.7953
Epoch 11/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8712 - val_loss: 0.7744
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8486 - val_loss: 0.7784
Epoch 13/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8350 - val_loss: 0.7483
Epoch 14/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8273 - val_loss: 0.7579
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8265 - val_loss: 0.7338
Epoch 16/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8272 - val_loss: 0.7622
Epoch 17/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8235 - val_loss: 0.7283
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8225 - val_loss: 0.7551
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8067 - val_loss: 0.7196
Epoch 20/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8069 - val_loss: 0.7164
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7939 - val_loss: 0.7516
Epoch 22/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7996 - val_loss: 0.7225
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7846 - val_loss: 0.7066
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7768 - val_loss: 0.7178
Epoch 25/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7809 - val_loss: 0.7095
Epoch 26/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7771 - val_loss: 0.7232
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7702 - val_loss: 0.7003
Epoch 28/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7727 - val_loss: 0.7232
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7631 - val_loss: 0.7028
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7660 - val_loss: 0.6962
Epoch 31/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7574 - val_loss: 0.7046
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7677 - val_loss: 0.6982
Epoch 33/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7585 - val_loss: 0.7191
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7531 - val_loss: 0.7086
Epoch 35/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7594 - val_loss: 0.7102
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7513 - val_loss: 0.7160
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7527 - val_loss: 0.7000
Epoch 38/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7471 - val_loss: 0.6911
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7405 - val_loss: 0.7010
Epoch 40/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7424 - val_loss: 0.6976
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7451 - val_loss: 0.7583
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7402 - val_loss: 0.7004
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7436 - val_loss: 0.6880
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7483 - val_loss: 0.6930
Epoch 45/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7316 - val_loss: 0.6885
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7398 - val_loss: 0.6992
Epoch 47/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7276 - val_loss: 0.7171
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7270 - val_loss: 0.6962
Epoch 49/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7324 - val_loss: 0.7041
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7272 - val_loss: 0.7001
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7202 - val_loss: 0.7016
Epoch 52/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7271 - val_loss: 0.6806
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7241 - val_loss: 0.6781
Epoch 54/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7222 - val_loss: 0.6967
Epoch 55/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7278 - val_loss: 0.6905
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7235 - val_loss: 0.6977
Epoch 57/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7328 - val_loss: 0.6802
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7089 - val_loss: 0.6767
Epoch 59/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7157 - val_loss: 0.7032
Epoch 60/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7324 - val_loss: 0.6830
Epoch 61/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7177 - val_loss: 0.6892
Epoch 62/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7143 - val_loss: 0.6975
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7022 - val_loss: 0.6909
Epoch 64/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7033 - val_loss: 0.6836
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7092 - val_loss: 0.6912
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7043 - val_loss: 0.6901
Epoch 67/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7056 - val_loss: 0.6728
Epoch 68/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7113 - val_loss: 0.6678
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7035 - val_loss: 0.6894
Epoch 70/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7053 - val_loss: 0.6812
Epoch 71/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7009 - val_loss: 0.6883
Epoch 72/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7026 - val_loss: 0.6820
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7033 - val_loss: 0.6786
Epoch 74/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7040 - val_loss: 0.6842
Epoch 75/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7100 - val_loss: 0.7083
Epoch 76/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6983 - val_loss: 0.6663
Epoch 77/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6968 - val_loss: 0.6591
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7018 - val_loss: 0.6737
Epoch 79/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6953 - val_loss: 0.6697
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7042 - val_loss: 0.6736
Epoch 81/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6924 - val_loss: 0.6790
Epoch 82/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6994 - val_loss: 0.6685
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7094 - val_loss: 0.6687
Epoch 84/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6980 - val_loss: 0.6790
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6941 - val_loss: 0.6979
Epoch 86/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6964 - val_loss: 0.6906
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7013 - val_loss: 0.6865
Epoch 88/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6897 - val_loss: 0.6693
Epoch 89/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6888 - val_loss: 0.6757
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6839 - val_loss: 0.6740
Epoch 91/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6964 - val_loss: 0.7073
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7002 - val_loss: 0.6710
Epoch 93/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6934 - val_loss: 0.6821
Epoch 94/100
24/24 [==============================] - 0s 5ms/step - loss: 0.6899 - val_loss: 0.6838
Epoch 95/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7000 - val_loss: 0.6635
Epoch 96/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6885 - val_loss: 0.6609
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6927 - val_loss: 0.6604
Epoch 98/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7052 - val_loss: 0.6748
Epoch 99/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6949 - val_loss: 0.6776
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6897 - val_loss: 0.6689
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_161 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_530 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_530 ( [()]                 0           tf_op_layer_Shape_530[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_53 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_530[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_53[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_159 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_53 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_159[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_531 (TensorFl [(2,)]               0           tf_op_layer_Add_53[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_533 (TensorFl [(2,)]               0           tf_op_layer_Add_53[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_532 (TensorFl [(2,)]               0           tf_op_layer_Add_53[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_534 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_536 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_535 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_537 (TensorFl [(2,)]               0           tf_op_layer_Add_53[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_539 (TensorFl [(2,)]               0           tf_op_layer_Add_53[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_538 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_531 ( [()]                 0           tf_op_layer_Shape_531[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_533 ( [()]                 0           tf_op_layer_Shape_533[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_532 ( [()]                 0           tf_op_layer_Shape_532[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_534 ( [()]                 0           tf_op_layer_Shape_534[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_536 ( [()]                 0           tf_op_layer_Shape_536[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_535 ( [()]                 0           tf_op_layer_Shape_535[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_537 ( [()]                 0           tf_op_layer_Shape_537[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_539 ( [()]                 0           tf_op_layer_Shape_539[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_538 ( [()]                 0           tf_op_layer_Shape_538[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_318/shape ( [(3,)]               0           tf_op_layer_strided_slice_531[0][
                                                                 tf_op_layer_strided_slice_533[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_319/shape ( [(3,)]               0           tf_op_layer_strided_slice_532[0][
                                                                 tf_op_layer_strided_slice_533[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_320/shape ( [(3,)]               0           tf_op_layer_strided_slice_534[0][
                                                                 tf_op_layer_strided_slice_536[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_321/shape ( [(3,)]               0           tf_op_layer_strided_slice_535[0][
                                                                 tf_op_layer_strided_slice_536[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_322/shape ( [(3,)]               0           tf_op_layer_strided_slice_537[0][
                                                                 tf_op_layer_strided_slice_539[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_323/shape ( [(3,)]               0           tf_op_layer_strided_slice_538[0][
                                                                 tf_op_layer_strided_slice_539[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_318 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_53[0][0]         
                                                                 tf_op_layer_Reshape_318/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_318/multiples  [(3,)]               0           tf_op_layer_strided_slice_532[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_319 (Tensor [(1, None, None)]    0           tf_op_layer_Add_53[0][0]         
                                                                 tf_op_layer_Reshape_319/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_319/multiples  [(3,)]               0           tf_op_layer_strided_slice_531[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_320 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_320/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_320/multiples  [(3,)]               0           tf_op_layer_strided_slice_535[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_321 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_321/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_321/multiples  [(3,)]               0           tf_op_layer_strided_slice_534[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_322 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_53[0][0]         
                                                                 tf_op_layer_Reshape_322/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_322/multiples  [(3,)]               0           tf_op_layer_strided_slice_538[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_323 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_323/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_323/multiples  [(3,)]               0           tf_op_layer_strided_slice_537[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_318 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_318[0][0]    
                                                                 tf_op_layer_Tile_318/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_319 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_319[0][0]    
                                                                 tf_op_layer_Tile_319/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_320 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_320[0][0]    
                                                                 tf_op_layer_Tile_320/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_321 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_321[0][0]    
                                                                 tf_op_layer_Tile_321/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_322 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_322[0][0]    
                                                                 tf_op_layer_Tile_322/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_323 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_323[0][0]    
                                                                 tf_op_layer_Tile_323/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_265 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_318[0][0]       
                                                                 tf_op_layer_Tile_319[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_266 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_320[0][0]       
                                                                 tf_op_layer_Tile_321[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_267 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_322[0][0]       
                                                                 tf_op_layer_Tile_323[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_212 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_265[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_213 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_266[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_214 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_267[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_371 (TensorFlo [(None, None)]       0           tf_op_layer_Square_212[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_372 (TensorFlo [(None, None)]       0           tf_op_layer_Square_213[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_373 (TensorFlo [(None, None)]       0           tf_op_layer_Square_214[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_159 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_371[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_159 (TensorFlo [()]                 0           tf_op_layer_strided_slice_533[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_160 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_372[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_160 (TensorFlo [()]                 0           tf_op_layer_strided_slice_536[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_161 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_373[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_161 (TensorFlo [()]                 0           tf_op_layer_strided_slice_539[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_159 (Tensor [(None, None)]       0           tf_op_layer_Neg_159[0][0]        
                                                                 tf_op_layer_Cast_159[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_160 (Tensor [(None, None)]       0           tf_op_layer_Neg_160[0][0]        
                                                                 tf_op_layer_Cast_160[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_161 (Tensor [(None, None)]       0           tf_op_layer_Neg_161[0][0]        
                                                                 tf_op_layer_Cast_161[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_159 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_159[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_160 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_160[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_161 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_161[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_374 (TensorFlo [()]                 0           tf_op_layer_Exp_159[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_375 (TensorFlo [()]                 0           tf_op_layer_Exp_160[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_376 (TensorFlo [()]                 0           tf_op_layer_Exp_161[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_269 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_106 (TensorFl [()]                 0           tf_op_layer_Mean_374[0][0]       
                                                                 tf_op_layer_Mean_375[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_160 (TensorFlow [()]                 0           tf_op_layer_Mean_376[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_215 (TensorF [(None, 97)]         0           tf_op_layer_Sub_269[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_268 (TensorFlow [()]                 0           tf_op_layer_AddV2_106[0][0]      
                                                                 tf_op_layer_Mul_160[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_53 (TensorFlowO [(None,)]            0           tf_op_layer_Square_215[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_161 (TensorFlow [()]                 0           tf_op_layer_Sub_268[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_107 (TensorFl [(None,)]            0           tf_op_layer_Sum_53[0][0]         
                                                                 tf_op_layer_Mul_161[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_377 (TensorFlo [()]                 0           tf_op_layer_AddV2_107[0][0]      
__________________________________________________________________________________________________
add_loss_53 (AddLoss)           ()                   0           tf_op_layer_Mean_377[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 3s - loss: 9.9136WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0019s vs `on_train_batch_end` time: 0.2796s). Check your callbacks.
24/24 [==============================] - 1s 38ms/step - loss: 7.6269 - val_loss: 5.1878
Epoch 2/100
24/24 [==============================] - 0s 8ms/step - loss: 3.9271 - val_loss: 3.0300
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 2.4934 - val_loss: 2.1878
Epoch 4/100
24/24 [==============================] - 0s 4ms/step - loss: 2.0336 - val_loss: 1.8357
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8194 - val_loss: 1.7105
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.7170 - val_loss: 1.6157
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6485 - val_loss: 1.6436
Epoch 8/100
24/24 [==============================] - 0s 5ms/step - loss: 1.6051 - val_loss: 1.5249
Epoch 9/100
24/24 [==============================] - 0s 4ms/step - loss: 1.5557 - val_loss: 1.5036
Epoch 10/100
24/24 [==============================] - 0s 4ms/step - loss: 1.5154 - val_loss: 1.5067
Epoch 11/100
24/24 [==============================] - 0s 8ms/step - loss: 1.4833 - val_loss: 1.4512
Epoch 12/100
24/24 [==============================] - 0s 4ms/step - loss: 1.4623 - val_loss: 1.4062
Epoch 13/100
24/24 [==============================] - 0s 4ms/step - loss: 1.4430 - val_loss: 1.4614
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3990 - val_loss: 1.3944
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3870 - val_loss: 1.3710
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3557 - val_loss: 1.3591
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3306 - val_loss: 1.3403
Epoch 18/100
24/24 [==============================] - 0s 4ms/step - loss: 1.3134 - val_loss: 1.2997
Epoch 19/100
24/24 [==============================] - 0s 5ms/step - loss: 1.3076 - val_loss: 1.2899
Epoch 20/100
24/24 [==============================] - 0s 5ms/step - loss: 1.2603 - val_loss: 1.2189
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2260 - val_loss: 1.2120
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1917 - val_loss: 1.2100
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1544 - val_loss: 1.2181
Epoch 24/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1377 - val_loss: 1.1721
Epoch 25/100
24/24 [==============================] - 0s 5ms/step - loss: 1.1231 - val_loss: 1.1354
Epoch 26/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1038 - val_loss: 1.1440
Epoch 27/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0804 - val_loss: 1.1263
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0841 - val_loss: 1.0860
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0883 - val_loss: 1.1043
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0605 - val_loss: 1.0975
Epoch 31/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0703 - val_loss: 1.1332
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0565 - val_loss: 1.0489
Epoch 33/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0552 - val_loss: 1.1215
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0585 - val_loss: 1.0886
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0437 - val_loss: 1.0771
Epoch 36/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0307 - val_loss: 1.0855
Epoch 37/100
24/24 [==============================] - 0s 5ms/step - loss: 1.0342 - val_loss: 1.0876
Epoch 38/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0334 - val_loss: 1.1105
Epoch 39/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0609 - val_loss: 1.0507
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0329 - val_loss: 1.0464
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0397 - val_loss: 1.0518
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0199 - val_loss: 1.0504
Epoch 43/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0242 - val_loss: 1.0949
Epoch 44/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0274 - val_loss: 1.0665
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0174 - val_loss: 1.0505
Epoch 46/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0165 - val_loss: 1.0739
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0195 - val_loss: 1.0600
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0069 - val_loss: 1.0641
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0152 - val_loss: 1.0419
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0155 - val_loss: 1.0441
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9955 - val_loss: 1.0405
Epoch 52/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0143 - val_loss: 1.0216
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9978 - val_loss: 1.0466
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9976 - val_loss: 1.0643
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9915 - val_loss: 1.0192
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9953 - val_loss: 1.0755
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9968 - val_loss: 1.0374
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0126 - val_loss: 1.0401
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0088 - val_loss: 1.0280
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9967 - val_loss: 1.0511
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9873 - val_loss: 1.0376
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9891 - val_loss: 1.0411
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9955 - val_loss: 1.0316
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9942 - val_loss: 1.0274
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9951 - val_loss: 1.0584
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9921 - val_loss: 1.0662
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9926 - val_loss: 1.0149
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9952 - val_loss: 1.0443
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9860 - val_loss: 1.0240
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9897 - val_loss: 1.0322
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9711 - val_loss: 1.0312
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0026 - val_loss: 1.0065
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9760 - val_loss: 1.0277
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9725 - val_loss: 1.0425
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9884 - val_loss: 1.0388
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9887 - val_loss: 1.0233
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9649 - val_loss: 1.0243
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9758 - val_loss: 1.0085
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9902 - val_loss: 1.0111
Epoch 80/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9754 - val_loss: 1.0140
Epoch 81/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9961 - val_loss: 1.0095
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9769 - val_loss: 1.0371
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9782 - val_loss: 1.0208
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9956 - val_loss: 0.9993
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9856 - val_loss: 1.0143
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9593 - val_loss: 1.0290
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9758 - val_loss: 1.0092
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9715 - val_loss: 1.0098
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9653 - val_loss: 1.0222
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9677 - val_loss: 1.0442
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9641 - val_loss: 1.0078
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9632 - val_loss: 1.0098
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9809 - val_loss: 0.9956
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9550 - val_loss: 1.0125
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9623 - val_loss: 1.0094
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9720 - val_loss: 1.0028
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9625 - val_loss: 0.9935
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9585 - val_loss: 1.0384
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9731 - val_loss: 1.0116
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9730 - val_loss: 1.0156
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.39 seconds.
    Calculating affinities...
    Calculated affinities in 0.02 seconds.
  Calculated graph and diffusion operator in 0.43 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 6.18 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.37 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 10.21 seconds.
Calculated PHATE in 17.20 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_162 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_163 (Dense)               (None, 32)           2080        dense_162[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_163[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_163[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_164 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_165 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_166 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_162 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_163 (Dense)               (None, 32)           2080        dense_162[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_163[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_163[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_540 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_540 ( [()]                 0           tf_op_layer_Shape_540[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_54 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_540[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_54[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_162 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_54 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_162[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_541 (TensorFl [(2,)]               0           tf_op_layer_Add_54[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_543 (TensorFl [(2,)]               0           tf_op_layer_Add_54[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_542 (TensorFl [(2,)]               0           tf_op_layer_Add_54[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_544 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_546 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_545 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_547 (TensorFl [(2,)]               0           tf_op_layer_Add_54[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_549 (TensorFl [(2,)]               0           tf_op_layer_Add_54[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_548 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_541 ( [()]                 0           tf_op_layer_Shape_541[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_543 ( [()]                 0           tf_op_layer_Shape_543[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_542 ( [()]                 0           tf_op_layer_Shape_542[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_544 ( [()]                 0           tf_op_layer_Shape_544[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_546 ( [()]                 0           tf_op_layer_Shape_546[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_545 ( [()]                 0           tf_op_layer_Shape_545[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_547 ( [()]                 0           tf_op_layer_Shape_547[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_549 ( [()]                 0           tf_op_layer_Shape_549[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_548 ( [()]                 0           tf_op_layer_Shape_548[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_324/shape ( [(3,)]               0           tf_op_layer_strided_slice_541[0][
                                                                 tf_op_layer_strided_slice_543[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_325/shape ( [(3,)]               0           tf_op_layer_strided_slice_542[0][
                                                                 tf_op_layer_strided_slice_543[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_326/shape ( [(3,)]               0           tf_op_layer_strided_slice_544[0][
                                                                 tf_op_layer_strided_slice_546[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_327/shape ( [(3,)]               0           tf_op_layer_strided_slice_545[0][
                                                                 tf_op_layer_strided_slice_546[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_328/shape ( [(3,)]               0           tf_op_layer_strided_slice_547[0][
                                                                 tf_op_layer_strided_slice_549[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_329/shape ( [(3,)]               0           tf_op_layer_strided_slice_548[0][
                                                                 tf_op_layer_strided_slice_549[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_324 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_54[0][0]         
                                                                 tf_op_layer_Reshape_324/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_324/multiples  [(3,)]               0           tf_op_layer_strided_slice_542[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_325 (Tensor [(1, None, None)]    0           tf_op_layer_Add_54[0][0]         
                                                                 tf_op_layer_Reshape_325/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_325/multiples  [(3,)]               0           tf_op_layer_strided_slice_541[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_326 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_326/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_326/multiples  [(3,)]               0           tf_op_layer_strided_slice_545[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_327 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_327/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_327/multiples  [(3,)]               0           tf_op_layer_strided_slice_544[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_328 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_54[0][0]         
                                                                 tf_op_layer_Reshape_328/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_328/multiples  [(3,)]               0           tf_op_layer_strided_slice_548[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_329 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_329/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_329/multiples  [(3,)]               0           tf_op_layer_strided_slice_547[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_324 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_324[0][0]    
                                                                 tf_op_layer_Tile_324/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_325 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_325[0][0]    
                                                                 tf_op_layer_Tile_325/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_326 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_326[0][0]    
                                                                 tf_op_layer_Tile_326/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_327 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_327[0][0]    
                                                                 tf_op_layer_Tile_327/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_328 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_328[0][0]    
                                                                 tf_op_layer_Tile_328/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_329 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_329[0][0]    
                                                                 tf_op_layer_Tile_329/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_270 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_324[0][0]       
                                                                 tf_op_layer_Tile_325[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_271 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_326[0][0]       
                                                                 tf_op_layer_Tile_327[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_272 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_328[0][0]       
                                                                 tf_op_layer_Tile_329[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_216 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_270[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_217 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_271[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_218 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_272[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_378 (TensorFlo [(None, None)]       0           tf_op_layer_Square_216[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_379 (TensorFlo [(None, None)]       0           tf_op_layer_Square_217[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_380 (TensorFlo [(None, None)]       0           tf_op_layer_Square_218[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_162 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_378[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_162 (TensorFlo [()]                 0           tf_op_layer_strided_slice_543[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_163 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_379[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_163 (TensorFlo [()]                 0           tf_op_layer_strided_slice_546[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_164 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_380[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_164 (TensorFlo [()]                 0           tf_op_layer_strided_slice_549[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_162 (Tensor [(None, None)]       0           tf_op_layer_Neg_162[0][0]        
                                                                 tf_op_layer_Cast_162[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_163 (Tensor [(None, None)]       0           tf_op_layer_Neg_163[0][0]        
                                                                 tf_op_layer_Cast_163[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_164 (Tensor [(None, None)]       0           tf_op_layer_Neg_164[0][0]        
                                                                 tf_op_layer_Cast_164[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_162 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_162[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_163 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_163[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_164 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_164[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_381 (TensorFlo [()]                 0           tf_op_layer_Exp_162[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_382 (TensorFlo [()]                 0           tf_op_layer_Exp_163[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_383 (TensorFlo [()]                 0           tf_op_layer_Exp_164[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_274 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_108 (TensorFl [()]                 0           tf_op_layer_Mean_381[0][0]       
                                                                 tf_op_layer_Mean_382[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_163 (TensorFlow [()]                 0           tf_op_layer_Mean_383[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_219 (TensorF [(None, 97)]         0           tf_op_layer_Sub_274[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_273 (TensorFlow [()]                 0           tf_op_layer_AddV2_108[0][0]      
                                                                 tf_op_layer_Mul_163[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_54 (TensorFlowO [(None,)]            0           tf_op_layer_Square_219[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_164 (TensorFlow [()]                 0           tf_op_layer_Sub_273[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_109 (TensorFl [(None,)]            0           tf_op_layer_Sum_54[0][0]         
                                                                 tf_op_layer_Mul_164[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_384 (TensorFlo [()]                 0           tf_op_layer_AddV2_109[0][0]      
__________________________________________________________________________________________________
add_loss_54 (AddLoss)           ()                   0           tf_op_layer_Mean_384[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 3s - loss: 9.9946 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0023s vs `on_train_batch_end` time: 0.3207s). Check your callbacks.
24/24 [==============================] - 1s 46ms/step - loss: 4.4199 - val_loss: 2.2555
Epoch 2/100
24/24 [==============================] - 0s 5ms/step - loss: 1.4654 - val_loss: 1.5738
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2164 - val_loss: 1.4711
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0877 - val_loss: 1.2664
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9556 - val_loss: 1.1553
Epoch 6/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9033 - val_loss: 1.0878
Epoch 7/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8779 - val_loss: 1.0904
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8518 - val_loss: 1.0386
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8399 - val_loss: 1.0149
Epoch 10/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8279 - val_loss: 1.0169
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8175 - val_loss: 0.9967
Epoch 12/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8076 - val_loss: 1.0021
Epoch 13/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7910 - val_loss: 0.9825
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8010 - val_loss: 0.9535
Epoch 15/100
24/24 [==============================] - 0s 4ms/step - loss: 0.8008 - val_loss: 0.9787
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8017 - val_loss: 0.9537
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7753 - val_loss: 0.9508
Epoch 18/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7784 - val_loss: 0.9487
Epoch 19/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7795 - val_loss: 0.9550
Epoch 20/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7760 - val_loss: 0.9442
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7733 - val_loss: 0.9450
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7661 - val_loss: 0.9273
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7662 - val_loss: 0.9247
Epoch 24/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7640 - val_loss: 0.9136
Epoch 25/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7532 - val_loss: 0.9210
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7499 - val_loss: 0.9206
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7464 - val_loss: 0.9107
Epoch 28/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7592 - val_loss: 0.9323
Epoch 29/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7551 - val_loss: 0.9139
Epoch 30/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7455 - val_loss: 0.9214
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7458 - val_loss: 0.9048
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7521 - val_loss: 0.8956
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7327 - val_loss: 0.8939
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7329 - val_loss: 0.9097
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7438 - val_loss: 0.8841
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7352 - val_loss: 0.9113
Epoch 37/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7303 - val_loss: 0.8885
Epoch 38/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7325 - val_loss: 0.8801
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7374 - val_loss: 0.9034
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7329 - val_loss: 0.8745
Epoch 41/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7258 - val_loss: 0.8913
Epoch 42/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7369 - val_loss: 0.8979
Epoch 43/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7219 - val_loss: 0.8728
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7331 - val_loss: 0.8809
Epoch 45/100
24/24 [==============================] - 0s 7ms/step - loss: 0.7189 - val_loss: 0.8715
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7229 - val_loss: 0.8851
Epoch 47/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7268 - val_loss: 0.8689
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7275 - val_loss: 0.8555
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7210 - val_loss: 0.8555
Epoch 50/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7359 - val_loss: 0.8682
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7231 - val_loss: 0.8663
Epoch 52/100
24/24 [==============================] - 0s 6ms/step - loss: 0.7148 - val_loss: 0.8673
Epoch 53/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7181 - val_loss: 0.8595
Epoch 54/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7123 - val_loss: 0.8592
Epoch 55/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7095 - val_loss: 0.8621
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7247 - val_loss: 0.8676
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7110 - val_loss: 0.8662
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7115 - val_loss: 0.8449
Epoch 59/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7197 - val_loss: 0.8673
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7166 - val_loss: 0.8760
Epoch 61/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7146 - val_loss: 0.8491
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7053 - val_loss: 0.8420
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7082 - val_loss: 0.8386
Epoch 64/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7180 - val_loss: 0.8525
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7041 - val_loss: 0.8624
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7086 - val_loss: 0.8570
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7047 - val_loss: 0.8436
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7034 - val_loss: 0.8518
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7102 - val_loss: 0.8537
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7057 - val_loss: 0.8565
Epoch 71/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7079 - val_loss: 0.8492
Epoch 72/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7006 - val_loss: 0.8393
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7080 - val_loss: 0.8545
Epoch 74/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7004 - val_loss: 0.8683
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7070 - val_loss: 0.8423
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6997 - val_loss: 0.8216
Epoch 77/100
24/24 [==============================] - 0s 6ms/step - loss: 0.7050 - val_loss: 0.8295
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7055 - val_loss: 0.8286
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6953 - val_loss: 0.8430
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7018 - val_loss: 0.8242
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7028 - val_loss: 0.8167
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7002 - val_loss: 0.8302
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7041 - val_loss: 0.8183
Epoch 84/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6920 - val_loss: 0.8462
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7039 - val_loss: 0.8441
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6894 - val_loss: 0.8196
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7017 - val_loss: 0.8160
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7203 - val_loss: 0.8166
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6917 - val_loss: 0.8139
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6963 - val_loss: 0.8264
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6906 - val_loss: 0.8264
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6839 - val_loss: 0.8166
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6914 - val_loss: 0.8116
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6932 - val_loss: 0.8186
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6829 - val_loss: 0.8084
Epoch 96/100
24/24 [==============================] - 0s 5ms/step - loss: 0.6882 - val_loss: 0.8066
Epoch 97/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6852 - val_loss: 0.8149
Epoch 98/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6872 - val_loss: 0.8057
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6895 - val_loss: 0.8285
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6829 - val_loss: 0.8197
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_167 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_550 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_550 ( [()]                 0           tf_op_layer_Shape_550[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_55 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_550[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_55[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_165 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_55 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_165[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_551 (TensorFl [(2,)]               0           tf_op_layer_Add_55[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_553 (TensorFl [(2,)]               0           tf_op_layer_Add_55[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_552 (TensorFl [(2,)]               0           tf_op_layer_Add_55[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_554 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_556 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_555 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_557 (TensorFl [(2,)]               0           tf_op_layer_Add_55[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_559 (TensorFl [(2,)]               0           tf_op_layer_Add_55[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_558 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_551 ( [()]                 0           tf_op_layer_Shape_551[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_553 ( [()]                 0           tf_op_layer_Shape_553[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_552 ( [()]                 0           tf_op_layer_Shape_552[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_554 ( [()]                 0           tf_op_layer_Shape_554[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_556 ( [()]                 0           tf_op_layer_Shape_556[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_555 ( [()]                 0           tf_op_layer_Shape_555[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_557 ( [()]                 0           tf_op_layer_Shape_557[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_559 ( [()]                 0           tf_op_layer_Shape_559[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_558 ( [()]                 0           tf_op_layer_Shape_558[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_330/shape ( [(3,)]               0           tf_op_layer_strided_slice_551[0][
                                                                 tf_op_layer_strided_slice_553[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_331/shape ( [(3,)]               0           tf_op_layer_strided_slice_552[0][
                                                                 tf_op_layer_strided_slice_553[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_332/shape ( [(3,)]               0           tf_op_layer_strided_slice_554[0][
                                                                 tf_op_layer_strided_slice_556[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_333/shape ( [(3,)]               0           tf_op_layer_strided_slice_555[0][
                                                                 tf_op_layer_strided_slice_556[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_334/shape ( [(3,)]               0           tf_op_layer_strided_slice_557[0][
                                                                 tf_op_layer_strided_slice_559[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_335/shape ( [(3,)]               0           tf_op_layer_strided_slice_558[0][
                                                                 tf_op_layer_strided_slice_559[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_330 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_55[0][0]         
                                                                 tf_op_layer_Reshape_330/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_330/multiples  [(3,)]               0           tf_op_layer_strided_slice_552[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_331 (Tensor [(1, None, None)]    0           tf_op_layer_Add_55[0][0]         
                                                                 tf_op_layer_Reshape_331/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_331/multiples  [(3,)]               0           tf_op_layer_strided_slice_551[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_332 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_332/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_332/multiples  [(3,)]               0           tf_op_layer_strided_slice_555[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_333 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_333/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_333/multiples  [(3,)]               0           tf_op_layer_strided_slice_554[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_334 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_55[0][0]         
                                                                 tf_op_layer_Reshape_334/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_334/multiples  [(3,)]               0           tf_op_layer_strided_slice_558[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_335 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_335/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_335/multiples  [(3,)]               0           tf_op_layer_strided_slice_557[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_330 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_330[0][0]    
                                                                 tf_op_layer_Tile_330/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_331 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_331[0][0]    
                                                                 tf_op_layer_Tile_331/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_332 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_332[0][0]    
                                                                 tf_op_layer_Tile_332/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_333 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_333[0][0]    
                                                                 tf_op_layer_Tile_333/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_334 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_334[0][0]    
                                                                 tf_op_layer_Tile_334/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_335 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_335[0][0]    
                                                                 tf_op_layer_Tile_335/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_275 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_330[0][0]       
                                                                 tf_op_layer_Tile_331[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_276 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_332[0][0]       
                                                                 tf_op_layer_Tile_333[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_277 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_334[0][0]       
                                                                 tf_op_layer_Tile_335[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_220 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_275[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_221 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_276[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_222 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_277[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_385 (TensorFlo [(None, None)]       0           tf_op_layer_Square_220[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_386 (TensorFlo [(None, None)]       0           tf_op_layer_Square_221[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_387 (TensorFlo [(None, None)]       0           tf_op_layer_Square_222[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_165 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_385[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_165 (TensorFlo [()]                 0           tf_op_layer_strided_slice_553[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_166 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_386[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_166 (TensorFlo [()]                 0           tf_op_layer_strided_slice_556[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_167 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_387[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_167 (TensorFlo [()]                 0           tf_op_layer_strided_slice_559[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_165 (Tensor [(None, None)]       0           tf_op_layer_Neg_165[0][0]        
                                                                 tf_op_layer_Cast_165[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_166 (Tensor [(None, None)]       0           tf_op_layer_Neg_166[0][0]        
                                                                 tf_op_layer_Cast_166[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_167 (Tensor [(None, None)]       0           tf_op_layer_Neg_167[0][0]        
                                                                 tf_op_layer_Cast_167[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_165 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_165[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_166 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_166[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_167 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_167[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_388 (TensorFlo [()]                 0           tf_op_layer_Exp_165[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_389 (TensorFlo [()]                 0           tf_op_layer_Exp_166[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_390 (TensorFlo [()]                 0           tf_op_layer_Exp_167[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_279 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_110 (TensorFl [()]                 0           tf_op_layer_Mean_388[0][0]       
                                                                 tf_op_layer_Mean_389[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_166 (TensorFlow [()]                 0           tf_op_layer_Mean_390[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_223 (TensorF [(None, 97)]         0           tf_op_layer_Sub_279[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_278 (TensorFlow [()]                 0           tf_op_layer_AddV2_110[0][0]      
                                                                 tf_op_layer_Mul_166[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_55 (TensorFlowO [(None,)]            0           tf_op_layer_Square_223[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_167 (TensorFlow [()]                 0           tf_op_layer_Sub_278[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_111 (TensorFl [(None,)]            0           tf_op_layer_Sum_55[0][0]         
                                                                 tf_op_layer_Mul_167[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_391 (TensorFlo [()]                 0           tf_op_layer_AddV2_111[0][0]      
__________________________________________________________________________________________________
add_loss_55 (AddLoss)           ()                   0           tf_op_layer_Mean_391[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 4s - loss: 10.2225WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0019s vs `on_train_batch_end` time: 0.3878s). Check your callbacks.
24/24 [==============================] - 1s 37ms/step - loss: 7.5928 - val_loss: 5.2126
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 3.9413 - val_loss: 3.1810
Epoch 3/100
24/24 [==============================] - 0s 3ms/step - loss: 2.4916 - val_loss: 2.4873
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.9930 - val_loss: 2.2344
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8268 - val_loss: 2.1105
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.7006 - val_loss: 2.0161
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.6099 - val_loss: 1.8832
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5352 - val_loss: 1.7894
Epoch 9/100
24/24 [==============================] - 0s 5ms/step - loss: 1.4512 - val_loss: 1.7937
Epoch 10/100
24/24 [==============================] - 0s 4ms/step - loss: 1.3883 - val_loss: 1.6343
Epoch 11/100
24/24 [==============================] - 0s 4ms/step - loss: 1.3330 - val_loss: 1.6061
Epoch 12/100
24/24 [==============================] - 0s 4ms/step - loss: 1.2594 - val_loss: 1.5943
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2386 - val_loss: 1.5297
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2250 - val_loss: 1.5254
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1865 - val_loss: 1.4974
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1646 - val_loss: 1.4608
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1678 - val_loss: 1.5205
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1314 - val_loss: 1.4039
Epoch 19/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1204 - val_loss: 1.4384
Epoch 20/100
24/24 [==============================] - 0s 6ms/step - loss: 1.1121 - val_loss: 1.3976
Epoch 21/100
24/24 [==============================] - 0s 4ms/step - loss: 1.1107 - val_loss: 1.3668
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1165 - val_loss: 1.3553
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1035 - val_loss: 1.3998
Epoch 24/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0881 - val_loss: 1.3649
Epoch 25/100
24/24 [==============================] - 0s 5ms/step - loss: 1.0997 - val_loss: 1.3884
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0766 - val_loss: 1.3352
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0742 - val_loss: 1.3366
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0690 - val_loss: 1.3111
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0738 - val_loss: 1.3177
Epoch 30/100
24/24 [==============================] - 0s 5ms/step - loss: 1.0747 - val_loss: 1.3163
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0484 - val_loss: 1.3415
Epoch 32/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0550 - val_loss: 1.3176
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0492 - val_loss: 1.3383
Epoch 34/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0591 - val_loss: 1.3305
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0341 - val_loss: 1.3380
Epoch 36/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0506 - val_loss: 1.3478
Epoch 37/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0470 - val_loss: 1.3325
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0361 - val_loss: 1.2744
Epoch 39/100
24/24 [==============================] - 0s 5ms/step - loss: 1.0265 - val_loss: 1.2780
Epoch 40/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0288 - val_loss: 1.2760
Epoch 41/100
24/24 [==============================] - 0s 6ms/step - loss: 1.0293 - val_loss: 1.3185
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0190 - val_loss: 1.2971
Epoch 43/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0075 - val_loss: 1.3081
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0357 - val_loss: 1.2408
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0255 - val_loss: 1.2813
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0246 - val_loss: 1.3300
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0159 - val_loss: 1.2409
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0053 - val_loss: 1.3286
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0263 - val_loss: 1.3349
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0019 - val_loss: 1.2471
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0141 - val_loss: 1.2736
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9973 - val_loss: 1.2600
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0164 - val_loss: 1.2757
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0164 - val_loss: 1.2320
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9953 - val_loss: 1.2440
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9999 - val_loss: 1.2624
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0074 - val_loss: 1.2149
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0118 - val_loss: 1.2870
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0049 - val_loss: 1.2909
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9972 - val_loss: 1.2423
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0032 - val_loss: 1.2525
Epoch 62/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9986 - val_loss: 1.2582
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9854 - val_loss: 1.2090
Epoch 64/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9945 - val_loss: 1.2302
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9883 - val_loss: 1.2356
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9838 - val_loss: 1.2060
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9993 - val_loss: 1.2549
Epoch 68/100
24/24 [==============================] - 0s 5ms/step - loss: 0.9981 - val_loss: 1.2023
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9887 - val_loss: 1.2525
Epoch 70/100
24/24 [==============================] - 0s 5ms/step - loss: 0.9861 - val_loss: 1.2000
Epoch 71/100
24/24 [==============================] - 0s 5ms/step - loss: 0.9887 - val_loss: 1.2410
Epoch 72/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9847 - val_loss: 1.2180
Epoch 73/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9895 - val_loss: 1.2560
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9713 - val_loss: 1.2003
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9960 - val_loss: 1.2125
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9837 - val_loss: 1.1950
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9768 - val_loss: 1.2238
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9709 - val_loss: 1.1968
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9801 - val_loss: 1.2156
Epoch 80/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9726 - val_loss: 1.2605
Epoch 81/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9879 - val_loss: 1.2088
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9629 - val_loss: 1.2279
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9734 - val_loss: 1.2069
Epoch 84/100
24/24 [==============================] - 0s 5ms/step - loss: 0.9790 - val_loss: 1.1985
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9781 - val_loss: 1.1849
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9812 - val_loss: 1.2257
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9666 - val_loss: 1.2186
Epoch 88/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9684 - val_loss: 1.2158
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9735 - val_loss: 1.2052
Epoch 90/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9611 - val_loss: 1.1996
Epoch 91/100
24/24 [==============================] - 0s 5ms/step - loss: 0.9641 - val_loss: 1.1675
Epoch 92/100
24/24 [==============================] - 0s 5ms/step - loss: 0.9643 - val_loss: 1.1923
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9658 - val_loss: 1.1798
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9682 - val_loss: 1.1858
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9624 - val_loss: 1.1906
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9801 - val_loss: 1.2152
Epoch 97/100
24/24 [==============================] - 0s 5ms/step - loss: 0.9752 - val_loss: 1.2580
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9578 - val_loss: 1.2230
Epoch 99/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9664 - val_loss: 1.2071
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9573 - val_loss: 1.1970
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.32 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.34 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.49 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.25 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 6.05 seconds.
Calculated PHATE in 8.14 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_168 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_169 (Dense)               (None, 32)           2080        dense_168[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_169[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_169[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_170 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_171 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_172 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_168 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_169 (Dense)               (None, 32)           2080        dense_168[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_169[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_169[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_560 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_560 ( [()]                 0           tf_op_layer_Shape_560[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_56 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_560[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_56[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_168 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_56 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_168[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_561 (TensorFl [(2,)]               0           tf_op_layer_Add_56[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_563 (TensorFl [(2,)]               0           tf_op_layer_Add_56[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_562 (TensorFl [(2,)]               0           tf_op_layer_Add_56[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_564 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_566 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_565 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_567 (TensorFl [(2,)]               0           tf_op_layer_Add_56[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_569 (TensorFl [(2,)]               0           tf_op_layer_Add_56[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_568 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_561 ( [()]                 0           tf_op_layer_Shape_561[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_563 ( [()]                 0           tf_op_layer_Shape_563[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_562 ( [()]                 0           tf_op_layer_Shape_562[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_564 ( [()]                 0           tf_op_layer_Shape_564[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_566 ( [()]                 0           tf_op_layer_Shape_566[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_565 ( [()]                 0           tf_op_layer_Shape_565[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_567 ( [()]                 0           tf_op_layer_Shape_567[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_569 ( [()]                 0           tf_op_layer_Shape_569[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_568 ( [()]                 0           tf_op_layer_Shape_568[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_336/shape ( [(3,)]               0           tf_op_layer_strided_slice_561[0][
                                                                 tf_op_layer_strided_slice_563[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_337/shape ( [(3,)]               0           tf_op_layer_strided_slice_562[0][
                                                                 tf_op_layer_strided_slice_563[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_338/shape ( [(3,)]               0           tf_op_layer_strided_slice_564[0][
                                                                 tf_op_layer_strided_slice_566[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_339/shape ( [(3,)]               0           tf_op_layer_strided_slice_565[0][
                                                                 tf_op_layer_strided_slice_566[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_340/shape ( [(3,)]               0           tf_op_layer_strided_slice_567[0][
                                                                 tf_op_layer_strided_slice_569[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_341/shape ( [(3,)]               0           tf_op_layer_strided_slice_568[0][
                                                                 tf_op_layer_strided_slice_569[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_336 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_56[0][0]         
                                                                 tf_op_layer_Reshape_336/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_336/multiples  [(3,)]               0           tf_op_layer_strided_slice_562[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_337 (Tensor [(1, None, None)]    0           tf_op_layer_Add_56[0][0]         
                                                                 tf_op_layer_Reshape_337/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_337/multiples  [(3,)]               0           tf_op_layer_strided_slice_561[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_338 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_338/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_338/multiples  [(3,)]               0           tf_op_layer_strided_slice_565[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_339 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_339/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_339/multiples  [(3,)]               0           tf_op_layer_strided_slice_564[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_340 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_56[0][0]         
                                                                 tf_op_layer_Reshape_340/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_340/multiples  [(3,)]               0           tf_op_layer_strided_slice_568[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_341 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_341/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_341/multiples  [(3,)]               0           tf_op_layer_strided_slice_567[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_336 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_336[0][0]    
                                                                 tf_op_layer_Tile_336/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_337 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_337[0][0]    
                                                                 tf_op_layer_Tile_337/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_338 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_338[0][0]    
                                                                 tf_op_layer_Tile_338/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_339 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_339[0][0]    
                                                                 tf_op_layer_Tile_339/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_340 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_340[0][0]    
                                                                 tf_op_layer_Tile_340/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_341 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_341[0][0]    
                                                                 tf_op_layer_Tile_341/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_280 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_336[0][0]       
                                                                 tf_op_layer_Tile_337[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_281 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_338[0][0]       
                                                                 tf_op_layer_Tile_339[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_282 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_340[0][0]       
                                                                 tf_op_layer_Tile_341[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_224 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_280[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_225 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_281[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_226 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_282[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_392 (TensorFlo [(None, None)]       0           tf_op_layer_Square_224[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_393 (TensorFlo [(None, None)]       0           tf_op_layer_Square_225[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_394 (TensorFlo [(None, None)]       0           tf_op_layer_Square_226[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_168 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_392[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_168 (TensorFlo [()]                 0           tf_op_layer_strided_slice_563[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_169 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_393[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_169 (TensorFlo [()]                 0           tf_op_layer_strided_slice_566[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_170 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_394[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_170 (TensorFlo [()]                 0           tf_op_layer_strided_slice_569[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_168 (Tensor [(None, None)]       0           tf_op_layer_Neg_168[0][0]        
                                                                 tf_op_layer_Cast_168[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_169 (Tensor [(None, None)]       0           tf_op_layer_Neg_169[0][0]        
                                                                 tf_op_layer_Cast_169[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_170 (Tensor [(None, None)]       0           tf_op_layer_Neg_170[0][0]        
                                                                 tf_op_layer_Cast_170[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_168 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_168[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_169 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_169[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_170 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_170[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_395 (TensorFlo [()]                 0           tf_op_layer_Exp_168[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_396 (TensorFlo [()]                 0           tf_op_layer_Exp_169[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_397 (TensorFlo [()]                 0           tf_op_layer_Exp_170[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_284 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_112 (TensorFl [()]                 0           tf_op_layer_Mean_395[0][0]       
                                                                 tf_op_layer_Mean_396[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_169 (TensorFlow [()]                 0           tf_op_layer_Mean_397[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_227 (TensorF [(None, 97)]         0           tf_op_layer_Sub_284[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_283 (TensorFlow [()]                 0           tf_op_layer_AddV2_112[0][0]      
                                                                 tf_op_layer_Mul_169[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_56 (TensorFlowO [(None,)]            0           tf_op_layer_Square_227[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_170 (TensorFlow [()]                 0           tf_op_layer_Sub_283[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_113 (TensorFl [(None,)]            0           tf_op_layer_Sum_56[0][0]         
                                                                 tf_op_layer_Mul_170[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_398 (TensorFlo [()]                 0           tf_op_layer_AddV2_113[0][0]      
__________________________________________________________________________________________________
add_loss_56 (AddLoss)           ()                   0           tf_op_layer_Mean_398[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.3002 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.1236s). Check your callbacks.
24/24 [==============================] - 0s 18ms/step - loss: 4.5483 - val_loss: 2.2247
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6785 - val_loss: 1.4173
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2751 - val_loss: 1.2814
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1622 - val_loss: 1.2132
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9953 - val_loss: 0.9875
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9078 - val_loss: 0.9690
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8900 - val_loss: 0.9291
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8588 - val_loss: 0.9742
Epoch 9/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8377 - val_loss: 0.8785
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8262 - val_loss: 0.8893
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8202 - val_loss: 0.9088
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8134 - val_loss: 0.8858
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8287 - val_loss: 0.9497
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8076 - val_loss: 0.8862
Epoch 15/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8124 - val_loss: 0.8880
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8101 - val_loss: 0.8826
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7994 - val_loss: 0.9015
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7819 - val_loss: 0.8640
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7978 - val_loss: 0.8671
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7791 - val_loss: 0.9390
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7844 - val_loss: 0.8784
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7789 - val_loss: 0.8435
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7719 - val_loss: 0.9143
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7799 - val_loss: 0.8667
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7766 - val_loss: 0.8613
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7746 - val_loss: 0.8424
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7607 - val_loss: 0.9044
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7623 - val_loss: 0.8724
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7536 - val_loss: 0.8542
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7565 - val_loss: 0.8489
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7498 - val_loss: 0.8303
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7549 - val_loss: 0.8512
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7470 - val_loss: 0.8395
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7501 - val_loss: 0.8663
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7497 - val_loss: 0.8388
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7521 - val_loss: 0.8343
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7520 - val_loss: 0.8872
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7382 - val_loss: 0.8574
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7508 - val_loss: 0.8600
Epoch 40/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7448 - val_loss: 0.8467
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7352 - val_loss: 0.8431
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7328 - val_loss: 0.8592
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7459 - val_loss: 0.8196
Epoch 44/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7377 - val_loss: 0.8643
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7380 - val_loss: 0.8280
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7329 - val_loss: 0.8348
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7338 - val_loss: 0.8392
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7385 - val_loss: 0.8356
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7367 - val_loss: 0.8468
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7392 - val_loss: 0.8244
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7278 - val_loss: 0.8671
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7346 - val_loss: 0.8312
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7271 - val_loss: 0.8076
Epoch 54/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7189 - val_loss: 0.8233
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7283 - val_loss: 0.8333
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7252 - val_loss: 0.8234
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7284 - val_loss: 0.8532
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7310 - val_loss: 0.8455
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7265 - val_loss: 0.8141
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7122 - val_loss: 0.8881
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7275 - val_loss: 0.8007
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7153 - val_loss: 0.8245
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7200 - val_loss: 0.8184
Epoch 64/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7082 - val_loss: 0.8541
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7143 - val_loss: 0.8675
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7192 - val_loss: 0.8244
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7206 - val_loss: 0.7980
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7076 - val_loss: 0.8172
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7120 - val_loss: 0.8409
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7047 - val_loss: 0.7966
Epoch 71/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7096 - val_loss: 0.8153
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7121 - val_loss: 0.8072
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7060 - val_loss: 0.8162
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7101 - val_loss: 0.8241
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7190 - val_loss: 0.8137
Epoch 76/100
24/24 [==============================] - 0s 5ms/step - loss: 0.7079 - val_loss: 0.8070
Epoch 77/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6996 - val_loss: 0.7868
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7028 - val_loss: 0.8639
Epoch 79/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6967 - val_loss: 0.7973
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7135 - val_loss: 0.7906
Epoch 81/100
24/24 [==============================] - 0s 4ms/step - loss: 0.6999 - val_loss: 0.8127
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6976 - val_loss: 0.8299
Epoch 83/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6966 - val_loss: 0.8272
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7007 - val_loss: 0.8395
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7017 - val_loss: 0.8134
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6985 - val_loss: 0.8342
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7023 - val_loss: 0.8244
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6947 - val_loss: 0.8241
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6970 - val_loss: 0.8080
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7005 - val_loss: 0.8743
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6948 - val_loss: 0.8108
Epoch 92/100
24/24 [==============================] - 0s 5ms/step - loss: 0.6911 - val_loss: 0.7947
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6854 - val_loss: 0.8662
Epoch 94/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6984 - val_loss: 0.8079
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6950 - val_loss: 0.7935
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7024 - val_loss: 0.8487
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6938 - val_loss: 0.7882
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6916 - val_loss: 0.8079
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6877 - val_loss: 0.8126
Epoch 100/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6958 - val_loss: 0.8130
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_173 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_570 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_570 ( [()]                 0           tf_op_layer_Shape_570[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_57 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_570[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_57[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_171 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_57 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_171[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_571 (TensorFl [(2,)]               0           tf_op_layer_Add_57[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_573 (TensorFl [(2,)]               0           tf_op_layer_Add_57[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_572 (TensorFl [(2,)]               0           tf_op_layer_Add_57[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_574 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_576 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_575 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_577 (TensorFl [(2,)]               0           tf_op_layer_Add_57[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_579 (TensorFl [(2,)]               0           tf_op_layer_Add_57[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_578 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_571 ( [()]                 0           tf_op_layer_Shape_571[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_573 ( [()]                 0           tf_op_layer_Shape_573[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_572 ( [()]                 0           tf_op_layer_Shape_572[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_574 ( [()]                 0           tf_op_layer_Shape_574[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_576 ( [()]                 0           tf_op_layer_Shape_576[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_575 ( [()]                 0           tf_op_layer_Shape_575[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_577 ( [()]                 0           tf_op_layer_Shape_577[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_579 ( [()]                 0           tf_op_layer_Shape_579[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_578 ( [()]                 0           tf_op_layer_Shape_578[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_342/shape ( [(3,)]               0           tf_op_layer_strided_slice_571[0][
                                                                 tf_op_layer_strided_slice_573[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_343/shape ( [(3,)]               0           tf_op_layer_strided_slice_572[0][
                                                                 tf_op_layer_strided_slice_573[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_344/shape ( [(3,)]               0           tf_op_layer_strided_slice_574[0][
                                                                 tf_op_layer_strided_slice_576[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_345/shape ( [(3,)]               0           tf_op_layer_strided_slice_575[0][
                                                                 tf_op_layer_strided_slice_576[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_346/shape ( [(3,)]               0           tf_op_layer_strided_slice_577[0][
                                                                 tf_op_layer_strided_slice_579[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_347/shape ( [(3,)]               0           tf_op_layer_strided_slice_578[0][
                                                                 tf_op_layer_strided_slice_579[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_342 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_57[0][0]         
                                                                 tf_op_layer_Reshape_342/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_342/multiples  [(3,)]               0           tf_op_layer_strided_slice_572[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_343 (Tensor [(1, None, None)]    0           tf_op_layer_Add_57[0][0]         
                                                                 tf_op_layer_Reshape_343/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_343/multiples  [(3,)]               0           tf_op_layer_strided_slice_571[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_344 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_344/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_344/multiples  [(3,)]               0           tf_op_layer_strided_slice_575[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_345 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_345/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_345/multiples  [(3,)]               0           tf_op_layer_strided_slice_574[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_346 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_57[0][0]         
                                                                 tf_op_layer_Reshape_346/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_346/multiples  [(3,)]               0           tf_op_layer_strided_slice_578[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_347 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_347/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_347/multiples  [(3,)]               0           tf_op_layer_strided_slice_577[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_342 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_342[0][0]    
                                                                 tf_op_layer_Tile_342/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_343 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_343[0][0]    
                                                                 tf_op_layer_Tile_343/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_344 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_344[0][0]    
                                                                 tf_op_layer_Tile_344/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_345 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_345[0][0]    
                                                                 tf_op_layer_Tile_345/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_346 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_346[0][0]    
                                                                 tf_op_layer_Tile_346/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_347 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_347[0][0]    
                                                                 tf_op_layer_Tile_347/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_285 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_342[0][0]       
                                                                 tf_op_layer_Tile_343[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_286 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_344[0][0]       
                                                                 tf_op_layer_Tile_345[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_287 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_346[0][0]       
                                                                 tf_op_layer_Tile_347[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_228 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_285[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_229 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_286[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_230 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_287[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_399 (TensorFlo [(None, None)]       0           tf_op_layer_Square_228[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_400 (TensorFlo [(None, None)]       0           tf_op_layer_Square_229[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_401 (TensorFlo [(None, None)]       0           tf_op_layer_Square_230[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_171 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_399[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_171 (TensorFlo [()]                 0           tf_op_layer_strided_slice_573[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_172 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_400[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_172 (TensorFlo [()]                 0           tf_op_layer_strided_slice_576[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_173 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_401[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_173 (TensorFlo [()]                 0           tf_op_layer_strided_slice_579[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_171 (Tensor [(None, None)]       0           tf_op_layer_Neg_171[0][0]        
                                                                 tf_op_layer_Cast_171[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_172 (Tensor [(None, None)]       0           tf_op_layer_Neg_172[0][0]        
                                                                 tf_op_layer_Cast_172[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_173 (Tensor [(None, None)]       0           tf_op_layer_Neg_173[0][0]        
                                                                 tf_op_layer_Cast_173[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_171 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_171[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_172 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_172[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_173 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_173[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_402 (TensorFlo [()]                 0           tf_op_layer_Exp_171[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_403 (TensorFlo [()]                 0           tf_op_layer_Exp_172[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_404 (TensorFlo [()]                 0           tf_op_layer_Exp_173[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_289 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_114 (TensorFl [()]                 0           tf_op_layer_Mean_402[0][0]       
                                                                 tf_op_layer_Mean_403[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_172 (TensorFlow [()]                 0           tf_op_layer_Mean_404[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_231 (TensorF [(None, 97)]         0           tf_op_layer_Sub_289[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_288 (TensorFlow [()]                 0           tf_op_layer_AddV2_114[0][0]      
                                                                 tf_op_layer_Mul_172[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_57 (TensorFlowO [(None,)]            0           tf_op_layer_Square_231[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_173 (TensorFlow [()]                 0           tf_op_layer_Sub_288[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_115 (TensorFl [(None,)]            0           tf_op_layer_Sum_57[0][0]         
                                                                 tf_op_layer_Mul_173[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_405 (TensorFlo [()]                 0           tf_op_layer_AddV2_115[0][0]      
__________________________________________________________________________________________________
add_loss_57 (AddLoss)           ()                   0           tf_op_layer_Mean_405[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 2s - loss: 10.3433WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.1895s). Check your callbacks.
24/24 [==============================] - 1s 23ms/step - loss: 7.8888 - val_loss: 5.0715
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 4.1491 - val_loss: 2.9540
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.5892 - val_loss: 2.0692
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.0513 - val_loss: 1.8688
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8462 - val_loss: 1.7515
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.7513 - val_loss: 1.6221
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6818 - val_loss: 1.5842
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6034 - val_loss: 1.5307
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5665 - val_loss: 1.5615
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5248 - val_loss: 1.5261
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 1.4899 - val_loss: 1.4644
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4607 - val_loss: 1.4534
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4441 - val_loss: 1.4263
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3940 - val_loss: 1.3949
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3976 - val_loss: 1.3698
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 1.3529 - val_loss: 1.3408
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3267 - val_loss: 1.3193
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2956 - val_loss: 1.3026
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2694 - val_loss: 1.3045
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2694 - val_loss: 1.2438
Epoch 21/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2239 - val_loss: 1.2561
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2044 - val_loss: 1.2071
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2046 - val_loss: 1.1777
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1700 - val_loss: 1.1555
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1650 - val_loss: 1.1760
Epoch 26/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1388 - val_loss: 1.1255
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1685 - val_loss: 1.1032
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1347 - val_loss: 1.1410
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1260 - val_loss: 1.0982
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1196 - val_loss: 1.0959
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1039 - val_loss: 1.0660
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1086 - val_loss: 1.1210
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1134 - val_loss: 1.0755
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0892 - val_loss: 1.0596
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0952 - val_loss: 1.0793
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0878 - val_loss: 1.1110
Epoch 37/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0950 - val_loss: 1.0233
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0734 - val_loss: 1.0393
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0728 - val_loss: 1.0464
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0731 - val_loss: 1.0600
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0779 - val_loss: 1.0327
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0723 - val_loss: 1.0240
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0519 - val_loss: 1.0105
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0625 - val_loss: 1.0211
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0654 - val_loss: 1.0463
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0579 - val_loss: 0.9936
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0462 - val_loss: 1.0128
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0454 - val_loss: 1.0146
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0379 - val_loss: 1.0312
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0381 - val_loss: 1.0364
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0315 - val_loss: 1.0169
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0509 - val_loss: 1.0386
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0419 - val_loss: 1.0018
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0205 - val_loss: 1.0131
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0279 - val_loss: 1.0456
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0445 - val_loss: 0.9929
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0271 - val_loss: 1.0541
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0205 - val_loss: 0.9947
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0270 - val_loss: 1.0045
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0262 - val_loss: 1.0179
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0132 - val_loss: 0.9865
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0269 - val_loss: 1.0176
Epoch 63/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0210 - val_loss: 0.9954
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0214 - val_loss: 1.0225
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0305 - val_loss: 0.9773
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0308 - val_loss: 0.9730
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0185 - val_loss: 1.0003
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0050 - val_loss: 0.9984
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0025 - val_loss: 0.9876
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0151 - val_loss: 1.0019
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0196 - val_loss: 0.9834
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0017 - val_loss: 0.9826
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0075 - val_loss: 1.0247
Epoch 74/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0078 - val_loss: 0.9773
Epoch 75/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9928 - val_loss: 0.9790
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0027 - val_loss: 0.9974
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0032 - val_loss: 0.9416
Epoch 78/100
24/24 [==============================] - 0s 5ms/step - loss: 0.9891 - val_loss: 0.9543
Epoch 79/100
24/24 [==============================] - 0s 4ms/step - loss: 1.0077 - val_loss: 0.9667
Epoch 80/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9953 - val_loss: 0.9573
Epoch 81/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9998 - val_loss: 0.9573
Epoch 82/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9854 - val_loss: 0.9762
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0163 - val_loss: 0.9702
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0033 - val_loss: 0.9828
Epoch 85/100
24/24 [==============================] - 0s 4ms/step - loss: 0.9889 - val_loss: 0.9406
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9861 - val_loss: 0.9806
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9848 - val_loss: 0.9697
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9938 - val_loss: 0.9549
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9838 - val_loss: 0.9966
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0039 - val_loss: 0.9744
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9806 - val_loss: 1.0042
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9930 - val_loss: 0.9809
Epoch 93/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9944 - val_loss: 0.9593
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0006 - val_loss: 0.9827
Epoch 95/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0002 - val_loss: 0.9733
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9940 - val_loss: 0.9613
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0002 - val_loss: 0.9596
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9885 - val_loss: 0.9356
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9981 - val_loss: 0.9606
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9917 - val_loss: 0.9585
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.40 seconds.
    Calculating affinities...
    Calculated affinities in 0.03 seconds.
  Calculated graph and diffusion operator in 0.44 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 2.50 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.24 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 6.05 seconds.
Calculated PHATE in 9.25 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_174 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_175 (Dense)               (None, 32)           2080        dense_174[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_175[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_175[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_176 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_177 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_178 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_174 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_175 (Dense)               (None, 32)           2080        dense_174[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_175[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_175[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_580 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_580 ( [()]                 0           tf_op_layer_Shape_580[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_58 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_580[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_58[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_174 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_58 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_174[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_581 (TensorFl [(2,)]               0           tf_op_layer_Add_58[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_583 (TensorFl [(2,)]               0           tf_op_layer_Add_58[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_582 (TensorFl [(2,)]               0           tf_op_layer_Add_58[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_584 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_586 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_585 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_587 (TensorFl [(2,)]               0           tf_op_layer_Add_58[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_589 (TensorFl [(2,)]               0           tf_op_layer_Add_58[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_588 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_581 ( [()]                 0           tf_op_layer_Shape_581[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_583 ( [()]                 0           tf_op_layer_Shape_583[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_582 ( [()]                 0           tf_op_layer_Shape_582[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_584 ( [()]                 0           tf_op_layer_Shape_584[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_586 ( [()]                 0           tf_op_layer_Shape_586[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_585 ( [()]                 0           tf_op_layer_Shape_585[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_587 ( [()]                 0           tf_op_layer_Shape_587[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_589 ( [()]                 0           tf_op_layer_Shape_589[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_588 ( [()]                 0           tf_op_layer_Shape_588[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_348/shape ( [(3,)]               0           tf_op_layer_strided_slice_581[0][
                                                                 tf_op_layer_strided_slice_583[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_349/shape ( [(3,)]               0           tf_op_layer_strided_slice_582[0][
                                                                 tf_op_layer_strided_slice_583[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_350/shape ( [(3,)]               0           tf_op_layer_strided_slice_584[0][
                                                                 tf_op_layer_strided_slice_586[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_351/shape ( [(3,)]               0           tf_op_layer_strided_slice_585[0][
                                                                 tf_op_layer_strided_slice_586[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_352/shape ( [(3,)]               0           tf_op_layer_strided_slice_587[0][
                                                                 tf_op_layer_strided_slice_589[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_353/shape ( [(3,)]               0           tf_op_layer_strided_slice_588[0][
                                                                 tf_op_layer_strided_slice_589[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_348 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_58[0][0]         
                                                                 tf_op_layer_Reshape_348/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_348/multiples  [(3,)]               0           tf_op_layer_strided_slice_582[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_349 (Tensor [(1, None, None)]    0           tf_op_layer_Add_58[0][0]         
                                                                 tf_op_layer_Reshape_349/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_349/multiples  [(3,)]               0           tf_op_layer_strided_slice_581[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_350 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_350/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_350/multiples  [(3,)]               0           tf_op_layer_strided_slice_585[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_351 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_351/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_351/multiples  [(3,)]               0           tf_op_layer_strided_slice_584[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_352 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_58[0][0]         
                                                                 tf_op_layer_Reshape_352/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_352/multiples  [(3,)]               0           tf_op_layer_strided_slice_588[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_353 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_353/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_353/multiples  [(3,)]               0           tf_op_layer_strided_slice_587[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_348 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_348[0][0]    
                                                                 tf_op_layer_Tile_348/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_349 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_349[0][0]    
                                                                 tf_op_layer_Tile_349/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_350 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_350[0][0]    
                                                                 tf_op_layer_Tile_350/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_351 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_351[0][0]    
                                                                 tf_op_layer_Tile_351/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_352 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_352[0][0]    
                                                                 tf_op_layer_Tile_352/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_353 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_353[0][0]    
                                                                 tf_op_layer_Tile_353/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_290 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_348[0][0]       
                                                                 tf_op_layer_Tile_349[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_291 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_350[0][0]       
                                                                 tf_op_layer_Tile_351[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_292 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_352[0][0]       
                                                                 tf_op_layer_Tile_353[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_232 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_290[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_233 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_291[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_234 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_292[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_406 (TensorFlo [(None, None)]       0           tf_op_layer_Square_232[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_407 (TensorFlo [(None, None)]       0           tf_op_layer_Square_233[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_408 (TensorFlo [(None, None)]       0           tf_op_layer_Square_234[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_174 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_406[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_174 (TensorFlo [()]                 0           tf_op_layer_strided_slice_583[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_175 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_407[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_175 (TensorFlo [()]                 0           tf_op_layer_strided_slice_586[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_176 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_408[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_176 (TensorFlo [()]                 0           tf_op_layer_strided_slice_589[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_174 (Tensor [(None, None)]       0           tf_op_layer_Neg_174[0][0]        
                                                                 tf_op_layer_Cast_174[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_175 (Tensor [(None, None)]       0           tf_op_layer_Neg_175[0][0]        
                                                                 tf_op_layer_Cast_175[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_176 (Tensor [(None, None)]       0           tf_op_layer_Neg_176[0][0]        
                                                                 tf_op_layer_Cast_176[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_174 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_174[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_175 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_175[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_176 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_176[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_409 (TensorFlo [()]                 0           tf_op_layer_Exp_174[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_410 (TensorFlo [()]                 0           tf_op_layer_Exp_175[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_411 (TensorFlo [()]                 0           tf_op_layer_Exp_176[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_294 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_116 (TensorFl [()]                 0           tf_op_layer_Mean_409[0][0]       
                                                                 tf_op_layer_Mean_410[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_175 (TensorFlow [()]                 0           tf_op_layer_Mean_411[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_235 (TensorF [(None, 97)]         0           tf_op_layer_Sub_294[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_293 (TensorFlow [()]                 0           tf_op_layer_AddV2_116[0][0]      
                                                                 tf_op_layer_Mul_175[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_58 (TensorFlowO [(None,)]            0           tf_op_layer_Square_235[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_176 (TensorFlow [()]                 0           tf_op_layer_Sub_293[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_117 (TensorFl [(None,)]            0           tf_op_layer_Sum_58[0][0]         
                                                                 tf_op_layer_Mul_176[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_412 (TensorFlo [()]                 0           tf_op_layer_AddV2_117[0][0]      
__________________________________________________________________________________________________
add_loss_58 (AddLoss)           ()                   0           tf_op_layer_Mean_412[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.8509 WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.1385s). Check your callbacks.
24/24 [==============================] - 0s 18ms/step - loss: 5.1651 - val_loss: 3.2140
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 2.5171 - val_loss: 1.7129
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4166 - val_loss: 1.3795
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2138 - val_loss: 1.2076
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1484 - val_loss: 1.1306
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0629 - val_loss: 0.9999
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9600 - val_loss: 0.9470
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9168 - val_loss: 0.9284
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8857 - val_loss: 0.9086
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8619 - val_loss: 0.8786
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8475 - val_loss: 0.8675
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8313 - val_loss: 0.8697
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8366 - val_loss: 0.8757
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8292 - val_loss: 0.8570
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8206 - val_loss: 0.8848
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8220 - val_loss: 0.8532
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8177 - val_loss: 0.8533
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8198 - val_loss: 0.8476
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8024 - val_loss: 0.8392
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8025 - val_loss: 0.8508
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8147 - val_loss: 0.8680
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7923 - val_loss: 0.8277
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7984 - val_loss: 0.8262
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7934 - val_loss: 0.8102
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7929 - val_loss: 0.8250
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7892 - val_loss: 0.8507
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7835 - val_loss: 0.8135
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7795 - val_loss: 0.8129
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7747 - val_loss: 0.8172
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7868 - val_loss: 0.8310
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7703 - val_loss: 0.7978
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7682 - val_loss: 0.8057
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7807 - val_loss: 0.8230
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7704 - val_loss: 0.7991
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7618 - val_loss: 0.8221
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7753 - val_loss: 0.8340
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7642 - val_loss: 0.8125
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7638 - val_loss: 0.8034
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7561 - val_loss: 0.8430
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7657 - val_loss: 0.8523
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7549 - val_loss: 0.8202
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7567 - val_loss: 0.7965
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7620 - val_loss: 0.8164
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7569 - val_loss: 0.8104
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7596 - val_loss: 0.8284
Epoch 46/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7507 - val_loss: 0.8013
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7524 - val_loss: 0.7926
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7390 - val_loss: 0.8040
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7498 - val_loss: 0.7932
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7444 - val_loss: 0.7915
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7407 - val_loss: 0.7783
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7545 - val_loss: 0.7909
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7387 - val_loss: 0.8476
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7525 - val_loss: 0.7942
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7321 - val_loss: 0.7965
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7352 - val_loss: 0.7759
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7477 - val_loss: 0.7746
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7323 - val_loss: 0.8563
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7421 - val_loss: 0.7961
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7533 - val_loss: 0.7885
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7331 - val_loss: 0.7628
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7328 - val_loss: 0.8121
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7259 - val_loss: 0.7723
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7320 - val_loss: 0.7856
Epoch 65/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7283 - val_loss: 0.7755
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7316 - val_loss: 0.7727
Epoch 67/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7277 - val_loss: 0.8033
Epoch 68/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7380 - val_loss: 0.7894
Epoch 69/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7276 - val_loss: 0.7717
Epoch 70/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7327 - val_loss: 0.8037
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7261 - val_loss: 0.7654
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7304 - val_loss: 0.8108
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7341 - val_loss: 0.7782
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7291 - val_loss: 0.8126
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7262 - val_loss: 0.8012
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7189 - val_loss: 0.7675
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7297 - val_loss: 0.7710
Epoch 78/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7197 - val_loss: 0.8018
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7349 - val_loss: 0.7717
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7169 - val_loss: 0.7804
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7237 - val_loss: 0.7640
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7153 - val_loss: 0.7874
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7261 - val_loss: 0.8141
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7194 - val_loss: 0.7536
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7218 - val_loss: 0.7684
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7118 - val_loss: 0.7833
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7150 - val_loss: 0.7914
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7236 - val_loss: 0.7911
Epoch 89/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7191 - val_loss: 0.7651
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7112 - val_loss: 0.7629
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7213 - val_loss: 0.8112
Epoch 92/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7108 - val_loss: 0.7788
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7129 - val_loss: 0.8076
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7168 - val_loss: 0.7873
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7042 - val_loss: 0.7890
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7136 - val_loss: 0.7527
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7175 - val_loss: 0.7761
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7185 - val_loss: 0.8014
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7061 - val_loss: 0.7795
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7300 - val_loss: 0.7858
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_179 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_590 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_590 ( [()]                 0           tf_op_layer_Shape_590[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_59 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_590[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_59[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_177 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_59 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_177[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_591 (TensorFl [(2,)]               0           tf_op_layer_Add_59[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_593 (TensorFl [(2,)]               0           tf_op_layer_Add_59[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_592 (TensorFl [(2,)]               0           tf_op_layer_Add_59[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_594 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_596 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_595 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_597 (TensorFl [(2,)]               0           tf_op_layer_Add_59[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_599 (TensorFl [(2,)]               0           tf_op_layer_Add_59[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_598 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_591 ( [()]                 0           tf_op_layer_Shape_591[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_593 ( [()]                 0           tf_op_layer_Shape_593[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_592 ( [()]                 0           tf_op_layer_Shape_592[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_594 ( [()]                 0           tf_op_layer_Shape_594[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_596 ( [()]                 0           tf_op_layer_Shape_596[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_595 ( [()]                 0           tf_op_layer_Shape_595[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_597 ( [()]                 0           tf_op_layer_Shape_597[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_599 ( [()]                 0           tf_op_layer_Shape_599[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_598 ( [()]                 0           tf_op_layer_Shape_598[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_354/shape ( [(3,)]               0           tf_op_layer_strided_slice_591[0][
                                                                 tf_op_layer_strided_slice_593[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_355/shape ( [(3,)]               0           tf_op_layer_strided_slice_592[0][
                                                                 tf_op_layer_strided_slice_593[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_356/shape ( [(3,)]               0           tf_op_layer_strided_slice_594[0][
                                                                 tf_op_layer_strided_slice_596[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_357/shape ( [(3,)]               0           tf_op_layer_strided_slice_595[0][
                                                                 tf_op_layer_strided_slice_596[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_358/shape ( [(3,)]               0           tf_op_layer_strided_slice_597[0][
                                                                 tf_op_layer_strided_slice_599[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_359/shape ( [(3,)]               0           tf_op_layer_strided_slice_598[0][
                                                                 tf_op_layer_strided_slice_599[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_354 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_59[0][0]         
                                                                 tf_op_layer_Reshape_354/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_354/multiples  [(3,)]               0           tf_op_layer_strided_slice_592[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_355 (Tensor [(1, None, None)]    0           tf_op_layer_Add_59[0][0]         
                                                                 tf_op_layer_Reshape_355/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_355/multiples  [(3,)]               0           tf_op_layer_strided_slice_591[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_356 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_356/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_356/multiples  [(3,)]               0           tf_op_layer_strided_slice_595[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_357 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_357/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_357/multiples  [(3,)]               0           tf_op_layer_strided_slice_594[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_358 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_59[0][0]         
                                                                 tf_op_layer_Reshape_358/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_358/multiples  [(3,)]               0           tf_op_layer_strided_slice_598[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_359 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_359/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_359/multiples  [(3,)]               0           tf_op_layer_strided_slice_597[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_354 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_354[0][0]    
                                                                 tf_op_layer_Tile_354/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_355 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_355[0][0]    
                                                                 tf_op_layer_Tile_355/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_356 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_356[0][0]    
                                                                 tf_op_layer_Tile_356/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_357 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_357[0][0]    
                                                                 tf_op_layer_Tile_357/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_358 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_358[0][0]    
                                                                 tf_op_layer_Tile_358/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_359 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_359[0][0]    
                                                                 tf_op_layer_Tile_359/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_295 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_354[0][0]       
                                                                 tf_op_layer_Tile_355[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_296 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_356[0][0]       
                                                                 tf_op_layer_Tile_357[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_297 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_358[0][0]       
                                                                 tf_op_layer_Tile_359[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_236 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_295[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_237 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_296[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_238 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_297[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_413 (TensorFlo [(None, None)]       0           tf_op_layer_Square_236[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_414 (TensorFlo [(None, None)]       0           tf_op_layer_Square_237[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_415 (TensorFlo [(None, None)]       0           tf_op_layer_Square_238[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_177 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_413[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_177 (TensorFlo [()]                 0           tf_op_layer_strided_slice_593[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_178 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_414[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_178 (TensorFlo [()]                 0           tf_op_layer_strided_slice_596[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_179 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_415[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_179 (TensorFlo [()]                 0           tf_op_layer_strided_slice_599[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_177 (Tensor [(None, None)]       0           tf_op_layer_Neg_177[0][0]        
                                                                 tf_op_layer_Cast_177[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_178 (Tensor [(None, None)]       0           tf_op_layer_Neg_178[0][0]        
                                                                 tf_op_layer_Cast_178[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_179 (Tensor [(None, None)]       0           tf_op_layer_Neg_179[0][0]        
                                                                 tf_op_layer_Cast_179[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_177 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_177[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_178 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_178[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_179 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_179[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_416 (TensorFlo [()]                 0           tf_op_layer_Exp_177[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_417 (TensorFlo [()]                 0           tf_op_layer_Exp_178[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_418 (TensorFlo [()]                 0           tf_op_layer_Exp_179[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_299 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_118 (TensorFl [()]                 0           tf_op_layer_Mean_416[0][0]       
                                                                 tf_op_layer_Mean_417[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_178 (TensorFlow [()]                 0           tf_op_layer_Mean_418[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_239 (TensorF [(None, 97)]         0           tf_op_layer_Sub_299[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_298 (TensorFlow [()]                 0           tf_op_layer_AddV2_118[0][0]      
                                                                 tf_op_layer_Mul_178[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_59 (TensorFlowO [(None,)]            0           tf_op_layer_Square_239[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_179 (TensorFlow [()]                 0           tf_op_layer_Sub_298[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_119 (TensorFl [(None,)]            0           tf_op_layer_Sum_59[0][0]         
                                                                 tf_op_layer_Mul_179[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_419 (TensorFlo [()]                 0           tf_op_layer_AddV2_119[0][0]      
__________________________________________________________________________________________________
add_loss_59 (AddLoss)           ()                   0           tf_op_layer_Mean_419[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.5394WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1399s). Check your callbacks.
24/24 [==============================] - 0s 18ms/step - loss: 8.1404 - val_loss: 5.5560
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.3208 - val_loss: 3.3586
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.7186 - val_loss: 2.4171
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.0633 - val_loss: 2.1080
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8203 - val_loss: 1.8860
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6715 - val_loss: 1.7966
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.5765 - val_loss: 1.7194
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4733 - val_loss: 1.5490
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3944 - val_loss: 1.4789
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3253 - val_loss: 1.4202
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2840 - val_loss: 1.3815
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2394 - val_loss: 1.3855
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2277 - val_loss: 1.3528
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1893 - val_loss: 1.3444
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1646 - val_loss: 1.2965
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1710 - val_loss: 1.3071
Epoch 17/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1646 - val_loss: 1.2729
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1354 - val_loss: 1.2756
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1284 - val_loss: 1.2372
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1195 - val_loss: 1.2719
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1179 - val_loss: 1.2341
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1106 - val_loss: 1.2315
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0858 - val_loss: 1.2719
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0778 - val_loss: 1.2261
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0800 - val_loss: 1.2106
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0645 - val_loss: 1.2278
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0714 - val_loss: 1.2267
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0631 - val_loss: 1.2368
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0554 - val_loss: 1.2279
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0598 - val_loss: 1.2076
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0640 - val_loss: 1.2076
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0717 - val_loss: 1.2323
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0555 - val_loss: 1.2366
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0491 - val_loss: 1.2265
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0385 - val_loss: 1.1993
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0522 - val_loss: 1.1781
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0282 - val_loss: 1.2095
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0427 - val_loss: 1.1940
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0357 - val_loss: 1.2475
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0465 - val_loss: 1.2181
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0256 - val_loss: 1.2146
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0127 - val_loss: 1.2208
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0210 - val_loss: 1.1661
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0235 - val_loss: 1.1814
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0181 - val_loss: 1.1635
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0189 - val_loss: 1.1800
Epoch 47/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0278 - val_loss: 1.2102
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0088 - val_loss: 1.1922
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0128 - val_loss: 1.1724
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0105 - val_loss: 1.1348
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0115 - val_loss: 1.1565
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0107 - val_loss: 1.1488
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0113 - val_loss: 1.1662
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0283 - val_loss: 1.1737
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0167 - val_loss: 1.1465
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9949 - val_loss: 1.1583
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0030 - val_loss: 1.1655
Epoch 58/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9999 - val_loss: 1.1784
Epoch 59/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0013 - val_loss: 1.1576
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9879 - val_loss: 1.1649
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9951 - val_loss: 1.1342
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9988 - val_loss: 1.1716
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9997 - val_loss: 1.1482
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0057 - val_loss: 1.1789
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9921 - val_loss: 1.1979
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9913 - val_loss: 1.1578
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9886 - val_loss: 1.1395
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9783 - val_loss: 1.1489
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0038 - val_loss: 1.1967
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9814 - val_loss: 1.1777
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0061 - val_loss: 1.1646
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9789 - val_loss: 1.1932
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9831 - val_loss: 1.1474
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9910 - val_loss: 1.1598
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9782 - val_loss: 1.1888
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9766 - val_loss: 1.1320
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9709 - val_loss: 1.1626
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9751 - val_loss: 1.1828
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9855 - val_loss: 1.1528
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9862 - val_loss: 1.1445
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9662 - val_loss: 1.1675
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9922 - val_loss: 1.1455
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9935 - val_loss: 1.2024
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9685 - val_loss: 1.1857
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9764 - val_loss: 1.1362
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9653 - val_loss: 1.1459
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9700 - val_loss: 1.1377
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9715 - val_loss: 1.1490
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9765 - val_loss: 1.1218
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9663 - val_loss: 1.1767
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9794 - val_loss: 1.1396
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9732 - val_loss: 1.1528
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9816 - val_loss: 1.1146
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9724 - val_loss: 1.1186
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9674 - val_loss: 1.1284
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9773 - val_loss: 1.1321
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9666 - val_loss: 1.1099
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9699 - val_loss: 1.1401
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9820 - val_loss: 1.1503
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9561 - val_loss: 1.1542
Calculating PHATE...
  Running PHATE on 1371 observations and 97 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.27 seconds.
    Calculating affinities...
    Calculated affinities in 0.01 seconds.
  Calculated graph and diffusion operator in 0.28 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 1.36 seconds.
  Calculating diffusion potential...
  Calculated diffusion potential in 0.23 seconds.
  Calculating metric MDS...
  Calculated metric MDS in 6.39 seconds.
Calculated PHATE in 8.28 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
dense_180 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_181 (Dense)               (None, 32)           2080        dense_180[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_181[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_181[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_182 (Dense)            (None, 32)                128       
_________________________________________________________________
dense_183 (Dense)            (None, 64)                2112      
_________________________________________________________________
dense_184 (Dense)            (None, 97)                6305      
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 8550        default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           8545        encoder[0][2]                    
__________________________________________________________________________________________________
dense_180 (Dense)               (None, 64)           6272        default_input[0][0]              
__________________________________________________________________________________________________
dense_181 (Dense)               (None, 32)           2080        dense_180[0][0]                  
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            99          dense_181[0][0]                  
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            99          dense_181[0][0]                  
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_600 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_600 ( [()]                 0           tf_op_layer_Shape_600[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_60 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_600[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_60[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_180 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_60 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_180[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_601 (TensorFl [(2,)]               0           tf_op_layer_Add_60[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_603 (TensorFl [(2,)]               0           tf_op_layer_Add_60[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_602 (TensorFl [(2,)]               0           tf_op_layer_Add_60[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_604 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_606 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_605 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_607 (TensorFl [(2,)]               0           tf_op_layer_Add_60[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_609 (TensorFl [(2,)]               0           tf_op_layer_Add_60[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_608 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_601 ( [()]                 0           tf_op_layer_Shape_601[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_603 ( [()]                 0           tf_op_layer_Shape_603[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_602 ( [()]                 0           tf_op_layer_Shape_602[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_604 ( [()]                 0           tf_op_layer_Shape_604[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_606 ( [()]                 0           tf_op_layer_Shape_606[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_605 ( [()]                 0           tf_op_layer_Shape_605[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_607 ( [()]                 0           tf_op_layer_Shape_607[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_609 ( [()]                 0           tf_op_layer_Shape_609[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_608 ( [()]                 0           tf_op_layer_Shape_608[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_360/shape ( [(3,)]               0           tf_op_layer_strided_slice_601[0][
                                                                 tf_op_layer_strided_slice_603[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_361/shape ( [(3,)]               0           tf_op_layer_strided_slice_602[0][
                                                                 tf_op_layer_strided_slice_603[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_362/shape ( [(3,)]               0           tf_op_layer_strided_slice_604[0][
                                                                 tf_op_layer_strided_slice_606[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_363/shape ( [(3,)]               0           tf_op_layer_strided_slice_605[0][
                                                                 tf_op_layer_strided_slice_606[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_364/shape ( [(3,)]               0           tf_op_layer_strided_slice_607[0][
                                                                 tf_op_layer_strided_slice_609[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_365/shape ( [(3,)]               0           tf_op_layer_strided_slice_608[0][
                                                                 tf_op_layer_strided_slice_609[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_360 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_60[0][0]         
                                                                 tf_op_layer_Reshape_360/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_360/multiples  [(3,)]               0           tf_op_layer_strided_slice_602[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_361 (Tensor [(1, None, None)]    0           tf_op_layer_Add_60[0][0]         
                                                                 tf_op_layer_Reshape_361/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_361/multiples  [(3,)]               0           tf_op_layer_strided_slice_601[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_362 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_362/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_362/multiples  [(3,)]               0           tf_op_layer_strided_slice_605[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_363 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_363/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_363/multiples  [(3,)]               0           tf_op_layer_strided_slice_604[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_364 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_60[0][0]         
                                                                 tf_op_layer_Reshape_364/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_364/multiples  [(3,)]               0           tf_op_layer_strided_slice_608[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_365 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_365/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_365/multiples  [(3,)]               0           tf_op_layer_strided_slice_607[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_360 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_360[0][0]    
                                                                 tf_op_layer_Tile_360/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_361 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_361[0][0]    
                                                                 tf_op_layer_Tile_361/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_362 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_362[0][0]    
                                                                 tf_op_layer_Tile_362/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_363 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_363[0][0]    
                                                                 tf_op_layer_Tile_363/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_364 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_364[0][0]    
                                                                 tf_op_layer_Tile_364/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_365 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_365[0][0]    
                                                                 tf_op_layer_Tile_365/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_300 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_360[0][0]       
                                                                 tf_op_layer_Tile_361[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_301 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_362[0][0]       
                                                                 tf_op_layer_Tile_363[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_302 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_364[0][0]       
                                                                 tf_op_layer_Tile_365[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_240 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_300[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_241 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_301[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_242 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_302[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_420 (TensorFlo [(None, None)]       0           tf_op_layer_Square_240[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_421 (TensorFlo [(None, None)]       0           tf_op_layer_Square_241[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_422 (TensorFlo [(None, None)]       0           tf_op_layer_Square_242[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_180 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_420[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_180 (TensorFlo [()]                 0           tf_op_layer_strided_slice_603[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_181 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_421[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_181 (TensorFlo [()]                 0           tf_op_layer_strided_slice_606[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_182 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_422[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_182 (TensorFlo [()]                 0           tf_op_layer_strided_slice_609[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_180 (Tensor [(None, None)]       0           tf_op_layer_Neg_180[0][0]        
                                                                 tf_op_layer_Cast_180[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_181 (Tensor [(None, None)]       0           tf_op_layer_Neg_181[0][0]        
                                                                 tf_op_layer_Cast_181[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_182 (Tensor [(None, None)]       0           tf_op_layer_Neg_182[0][0]        
                                                                 tf_op_layer_Cast_182[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_180 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_180[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_181 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_181[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_182 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_182[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_423 (TensorFlo [()]                 0           tf_op_layer_Exp_180[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_424 (TensorFlo [()]                 0           tf_op_layer_Exp_181[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_425 (TensorFlo [()]                 0           tf_op_layer_Exp_182[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_304 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_120 (TensorFl [()]                 0           tf_op_layer_Mean_423[0][0]       
                                                                 tf_op_layer_Mean_424[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_181 (TensorFlow [()]                 0           tf_op_layer_Mean_425[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_243 (TensorF [(None, 97)]         0           tf_op_layer_Sub_304[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_303 (TensorFlow [()]                 0           tf_op_layer_AddV2_120[0][0]      
                                                                 tf_op_layer_Mul_181[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_60 (TensorFlowO [(None,)]            0           tf_op_layer_Square_243[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_182 (TensorFlow [()]                 0           tf_op_layer_Sub_303[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_121 (TensorFl [(None,)]            0           tf_op_layer_Sum_60[0][0]         
                                                                 tf_op_layer_Mul_182[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_426 (TensorFlo [()]                 0           tf_op_layer_AddV2_121[0][0]      
__________________________________________________________________________________________________
add_loss_60 (AddLoss)           ()                   0           tf_op_layer_Mean_426[0][0]       
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 9.3704WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.1589s). Check your callbacks.
24/24 [==============================] - 1s 23ms/step - loss: 5.1003 - val_loss: 2.8131
Epoch 2/100
24/24 [==============================] - 0s 3ms/step - loss: 1.8362 - val_loss: 1.5778
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3107 - val_loss: 1.3928
Epoch 4/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1994 - val_loss: 1.3065
Epoch 5/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1463 - val_loss: 1.2444
Epoch 6/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1278 - val_loss: 1.2352
Epoch 7/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0851 - val_loss: 1.1912
Epoch 8/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0570 - val_loss: 1.1600
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0118 - val_loss: 1.0679
Epoch 10/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9792 - val_loss: 1.0170
Epoch 11/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9186 - val_loss: 0.9620
Epoch 12/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8872 - val_loss: 0.9310
Epoch 13/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8691 - val_loss: 0.8914
Epoch 14/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8276 - val_loss: 0.8879
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 0.8142 - val_loss: 0.8874
Epoch 16/100
24/24 [==============================] - 0s 3ms/step - loss: 0.8064 - val_loss: 0.8888
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7990 - val_loss: 0.9027
Epoch 18/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7858 - val_loss: 0.8847
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7806 - val_loss: 0.9154
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7785 - val_loss: 0.8868
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7813 - val_loss: 0.8844
Epoch 22/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7744 - val_loss: 0.8761
Epoch 23/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7741 - val_loss: 0.8891
Epoch 24/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7507 - val_loss: 0.8709
Epoch 25/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7525 - val_loss: 0.8620
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7549 - val_loss: 0.8718
Epoch 27/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7545 - val_loss: 0.8686
Epoch 28/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7558 - val_loss: 0.8493
Epoch 29/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7430 - val_loss: 0.8485
Epoch 30/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7452 - val_loss: 0.8327
Epoch 31/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7301 - val_loss: 0.8300
Epoch 32/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7397 - val_loss: 0.8395
Epoch 33/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7462 - val_loss: 0.8360
Epoch 34/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7288 - val_loss: 0.8388
Epoch 35/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7307 - val_loss: 0.8164
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7230 - val_loss: 0.8257
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7311 - val_loss: 0.8507
Epoch 38/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7163 - val_loss: 0.8845
Epoch 39/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7308 - val_loss: 0.8408
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7185 - val_loss: 0.8679
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7240 - val_loss: 0.8540
Epoch 42/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7185 - val_loss: 0.8136
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7159 - val_loss: 0.8133
Epoch 44/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7118 - val_loss: 0.8373
Epoch 45/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7209 - val_loss: 0.8226
Epoch 46/100
24/24 [==============================] - 0s 4ms/step - loss: 0.7096 - val_loss: 0.8729
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7159 - val_loss: 0.8151
Epoch 48/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7264 - val_loss: 0.8245
Epoch 49/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7107 - val_loss: 0.8247
Epoch 50/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7145 - val_loss: 0.8512
Epoch 51/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7105 - val_loss: 0.8113
Epoch 52/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7141 - val_loss: 0.8038
Epoch 53/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7035 - val_loss: 0.8454
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7119 - val_loss: 0.8408
Epoch 55/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7081 - val_loss: 0.8540
Epoch 56/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7031 - val_loss: 0.8234
Epoch 57/100
24/24 [==============================] - 0s 3ms/step - loss: 0.7052 - val_loss: 0.8108
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6999 - val_loss: 0.7994
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6988 - val_loss: 0.8345
Epoch 60/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6957 - val_loss: 0.8014
Epoch 61/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6943 - val_loss: 0.8076
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7029 - val_loss: 0.8376
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6971 - val_loss: 0.8012
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6981 - val_loss: 0.8177
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6939 - val_loss: 0.7841
Epoch 66/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6887 - val_loss: 0.8410
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6940 - val_loss: 0.8240
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6889 - val_loss: 0.8310
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6940 - val_loss: 0.8118
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6892 - val_loss: 0.8015
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6893 - val_loss: 0.8171
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6833 - val_loss: 0.8051
Epoch 73/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6986 - val_loss: 0.8123
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6840 - val_loss: 0.8066
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6850 - val_loss: 0.7821
Epoch 76/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6897 - val_loss: 0.8305
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6830 - val_loss: 0.8020
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6917 - val_loss: 0.7947
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.7068 - val_loss: 0.8027
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6881 - val_loss: 0.8125
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6928 - val_loss: 0.8024
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6984 - val_loss: 0.8101
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6920 - val_loss: 0.8005
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6852 - val_loss: 0.7969
Epoch 85/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6997 - val_loss: 0.8035
Epoch 86/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6858 - val_loss: 0.8331
Epoch 87/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6864 - val_loss: 0.8368
Epoch 88/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6864 - val_loss: 0.8239
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6906 - val_loss: 0.8186
Epoch 90/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6777 - val_loss: 0.8076
Epoch 91/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6839 - val_loss: 0.7986
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6791 - val_loss: 0.7941
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6881 - val_loss: 0.7847
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6970 - val_loss: 0.8096
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6745 - val_loss: 0.8052
Epoch 96/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6749 - val_loss: 0.8011
Epoch 97/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6766 - val_loss: 0.8023
Epoch 98/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6729 - val_loss: 0.8117
Epoch 99/100
24/24 [==============================] - 0s 3ms/step - loss: 0.6729 - val_loss: 0.7947
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.6749 - val_loss: 0.8084
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
z_sampling (InputLayer)      [(None, 3)]               0         
_________________________________________________________________
dense_185 (Dense)            (None, 97)                388       
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
default_input (InputLayer)      [(None, 97)]         0                                            
__________________________________________________________________________________________________
encoder (Functional)            [(None, 3), (None, 3 588         default_input[0][0]              
__________________________________________________________________________________________________
decoder (Functional)            (None, 97)           388         encoder[0][2]                    
__________________________________________________________________________________________________
z_mean (Dense)                  (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z_log_sigma (Dense)             (None, 3)            294         default_input[0][0]              
__________________________________________________________________________________________________
z (Lambda)                      (None, 3)            0           z_mean[0][0]                     
                                                                 z_log_sigma[0][0]                
__________________________________________________________________________________________________
tf_op_layer_Shape_610 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_610 ( [()]                 0           tf_op_layer_Shape_610[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Pack_61 (TensorFlow [(2,)]               0           tf_op_layer_strided_slice_610[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)]          0           tf_op_layer_Pack_61[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mul_183 (TensorFlow [(None, 3)]          0           tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_61 (TensorFlowO [(None, 3)]          0           tf_op_layer_Mul_183[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Shape_611 (TensorFl [(2,)]               0           tf_op_layer_Add_61[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_613 (TensorFl [(2,)]               0           tf_op_layer_Add_61[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_612 (TensorFl [(2,)]               0           tf_op_layer_Add_61[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_614 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_616 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_615 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_Shape_617 (TensorFl [(2,)]               0           tf_op_layer_Add_61[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_619 (TensorFl [(2,)]               0           tf_op_layer_Add_61[0][0]         
__________________________________________________________________________________________________
tf_op_layer_Shape_618 (TensorFl [(2,)]               0           z[0][0]                          
__________________________________________________________________________________________________
tf_op_layer_strided_slice_611 ( [()]                 0           tf_op_layer_Shape_611[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_613 ( [()]                 0           tf_op_layer_Shape_613[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_612 ( [()]                 0           tf_op_layer_Shape_612[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_614 ( [()]                 0           tf_op_layer_Shape_614[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_616 ( [()]                 0           tf_op_layer_Shape_616[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_615 ( [()]                 0           tf_op_layer_Shape_615[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_617 ( [()]                 0           tf_op_layer_Shape_617[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_619 ( [()]                 0           tf_op_layer_Shape_619[0][0]      
__________________________________________________________________________________________________
tf_op_layer_strided_slice_618 ( [()]                 0           tf_op_layer_Shape_618[0][0]      
__________________________________________________________________________________________________
tf_op_layer_Reshape_366/shape ( [(3,)]               0           tf_op_layer_strided_slice_611[0][
                                                                 tf_op_layer_strided_slice_613[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_367/shape ( [(3,)]               0           tf_op_layer_strided_slice_612[0][
                                                                 tf_op_layer_strided_slice_613[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_368/shape ( [(3,)]               0           tf_op_layer_strided_slice_614[0][
                                                                 tf_op_layer_strided_slice_616[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_369/shape ( [(3,)]               0           tf_op_layer_strided_slice_615[0][
                                                                 tf_op_layer_strided_slice_616[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_370/shape ( [(3,)]               0           tf_op_layer_strided_slice_617[0][
                                                                 tf_op_layer_strided_slice_619[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_371/shape ( [(3,)]               0           tf_op_layer_strided_slice_618[0][
                                                                 tf_op_layer_strided_slice_619[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_366 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_61[0][0]         
                                                                 tf_op_layer_Reshape_366/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_366/multiples  [(3,)]               0           tf_op_layer_strided_slice_612[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_367 (Tensor [(1, None, None)]    0           tf_op_layer_Add_61[0][0]         
                                                                 tf_op_layer_Reshape_367/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_367/multiples  [(3,)]               0           tf_op_layer_strided_slice_611[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_368 (Tensor [(None, 1, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_368/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_368/multiples  [(3,)]               0           tf_op_layer_strided_slice_615[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_369 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_369/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_369/multiples  [(3,)]               0           tf_op_layer_strided_slice_614[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_370 (Tensor [(None, 1, None)]    0           tf_op_layer_Add_61[0][0]         
                                                                 tf_op_layer_Reshape_370/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_370/multiples  [(3,)]               0           tf_op_layer_strided_slice_618[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_371 (Tensor [(1, None, None)]    0           z[0][0]                          
                                                                 tf_op_layer_Reshape_371/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_371/multiples  [(3,)]               0           tf_op_layer_strided_slice_617[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_366 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_366[0][0]    
                                                                 tf_op_layer_Tile_366/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_367 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_367[0][0]    
                                                                 tf_op_layer_Tile_367/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_368 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_368[0][0]    
                                                                 tf_op_layer_Tile_368/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_369 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_369[0][0]    
                                                                 tf_op_layer_Tile_369/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_370 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_370[0][0]    
                                                                 tf_op_layer_Tile_370/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_371 (TensorFlo [(None, None, None)] 0           tf_op_layer_Reshape_371[0][0]    
                                                                 tf_op_layer_Tile_371/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_305 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_366[0][0]       
                                                                 tf_op_layer_Tile_367[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_306 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_368[0][0]       
                                                                 tf_op_layer_Tile_369[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Sub_307 (TensorFlow [(None, None, None)] 0           tf_op_layer_Tile_370[0][0]       
                                                                 tf_op_layer_Tile_371[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_244 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_305[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_245 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_306[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Square_246 (TensorF [(None, None, None)] 0           tf_op_layer_Sub_307[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_427 (TensorFlo [(None, None)]       0           tf_op_layer_Square_244[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_428 (TensorFlo [(None, None)]       0           tf_op_layer_Square_245[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mean_429 (TensorFlo [(None, None)]       0           tf_op_layer_Square_246[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Neg_183 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_427[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_183 (TensorFlo [()]                 0           tf_op_layer_strided_slice_613[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_184 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_428[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_184 (TensorFlo [()]                 0           tf_op_layer_strided_slice_616[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_185 (TensorFlow [(None, None)]       0           tf_op_layer_Mean_429[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Cast_185 (TensorFlo [()]                 0           tf_op_layer_strided_slice_619[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_183 (Tensor [(None, None)]       0           tf_op_layer_Neg_183[0][0]        
                                                                 tf_op_layer_Cast_183[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_184 (Tensor [(None, None)]       0           tf_op_layer_Neg_184[0][0]        
                                                                 tf_op_layer_Cast_184[0][0]       
__________________________________________________________________________________________________
tf_op_layer_RealDiv_185 (Tensor [(None, None)]       0           tf_op_layer_Neg_185[0][0]        
                                                                 tf_op_layer_Cast_185[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Exp_183 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_183[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_184 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_184[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Exp_185 (TensorFlow [(None, None)]       0           tf_op_layer_RealDiv_185[0][0]    
__________________________________________________________________________________________________
tf_op_layer_Mean_430 (TensorFlo [()]                 0           tf_op_layer_Exp_183[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_431 (TensorFlo [()]                 0           tf_op_layer_Exp_184[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_432 (TensorFlo [()]                 0           tf_op_layer_Exp_185[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_309 (TensorFlow [(None, 97)]         0           default_input[0][0]              
                                                                 decoder[0][0]                    
__________________________________________________________________________________________________
tf_op_layer_AddV2_122 (TensorFl [()]                 0           tf_op_layer_Mean_430[0][0]       
                                                                 tf_op_layer_Mean_431[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Mul_184 (TensorFlow [()]                 0           tf_op_layer_Mean_432[0][0]       
__________________________________________________________________________________________________
tf_op_layer_Square_247 (TensorF [(None, 97)]         0           tf_op_layer_Sub_309[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sub_308 (TensorFlow [()]                 0           tf_op_layer_AddV2_122[0][0]      
                                                                 tf_op_layer_Mul_184[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Sum_61 (TensorFlowO [(None,)]            0           tf_op_layer_Square_247[0][0]     
__________________________________________________________________________________________________
tf_op_layer_Mul_185 (TensorFlow [()]                 0           tf_op_layer_Sub_308[0][0]        
__________________________________________________________________________________________________
tf_op_layer_AddV2_123 (TensorFl [(None,)]            0           tf_op_layer_Sum_61[0][0]         
                                                                 tf_op_layer_Mul_185[0][0]        
__________________________________________________________________________________________________
tf_op_layer_Mean_433 (TensorFlo [()]                 0           tf_op_layer_AddV2_123[0][0]      
__________________________________________________________________________________________________
add_loss_61 (AddLoss)           ()                   0           tf_op_layer_Mean_433[0][0]       
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
 2/24 [=>............................] - ETA: 1s - loss: 10.9309WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1698s). Check your callbacks.
24/24 [==============================] - 1s 22ms/step - loss: 7.7968 - val_loss: 5.5901
Epoch 2/100
24/24 [==============================] - 0s 2ms/step - loss: 4.1174 - val_loss: 3.3119
Epoch 3/100
24/24 [==============================] - 0s 2ms/step - loss: 2.5436 - val_loss: 2.4074
Epoch 4/100
24/24 [==============================] - 0s 2ms/step - loss: 2.0010 - val_loss: 2.1244
Epoch 5/100
24/24 [==============================] - 0s 2ms/step - loss: 1.8149 - val_loss: 1.9661
Epoch 6/100
24/24 [==============================] - 0s 2ms/step - loss: 1.6920 - val_loss: 1.8114
Epoch 7/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5873 - val_loss: 1.6856
Epoch 8/100
24/24 [==============================] - 0s 2ms/step - loss: 1.5110 - val_loss: 1.6345
Epoch 9/100
24/24 [==============================] - 0s 2ms/step - loss: 1.4196 - val_loss: 1.4890
Epoch 10/100
24/24 [==============================] - 0s 2ms/step - loss: 1.3502 - val_loss: 1.4435
Epoch 11/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2970 - val_loss: 1.3448
Epoch 12/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2443 - val_loss: 1.2914
Epoch 13/100
24/24 [==============================] - 0s 2ms/step - loss: 1.2302 - val_loss: 1.2824
Epoch 14/100
24/24 [==============================] - 0s 3ms/step - loss: 1.2065 - val_loss: 1.2987
Epoch 15/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1794 - val_loss: 1.2753
Epoch 16/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1593 - val_loss: 1.2680
Epoch 17/100
24/24 [==============================] - 0s 3ms/step - loss: 1.1501 - val_loss: 1.2243
Epoch 18/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1303 - val_loss: 1.2414
Epoch 19/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1157 - val_loss: 1.2179
Epoch 20/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1121 - val_loss: 1.1871
Epoch 21/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1170 - val_loss: 1.1824
Epoch 22/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1205 - val_loss: 1.1794
Epoch 23/100
24/24 [==============================] - 0s 2ms/step - loss: 1.1065 - val_loss: 1.1695
Epoch 24/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0900 - val_loss: 1.1980
Epoch 25/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0891 - val_loss: 1.2011
Epoch 26/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0870 - val_loss: 1.1473
Epoch 27/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0621 - val_loss: 1.1565
Epoch 28/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0598 - val_loss: 1.1861
Epoch 29/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0460 - val_loss: 1.1307
Epoch 30/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0535 - val_loss: 1.1536
Epoch 31/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0687 - val_loss: 1.1497
Epoch 32/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0511 - val_loss: 1.1643
Epoch 33/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0488 - val_loss: 1.0959
Epoch 34/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0403 - val_loss: 1.1153
Epoch 35/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0454 - val_loss: 1.1621
Epoch 36/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0357 - val_loss: 1.1274
Epoch 37/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0317 - val_loss: 1.1024
Epoch 38/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0467 - val_loss: 1.1565
Epoch 39/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0338 - val_loss: 1.1135
Epoch 40/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0230 - val_loss: 1.0901
Epoch 41/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0247 - val_loss: 1.0911
Epoch 42/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0281 - val_loss: 1.1005
Epoch 43/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0338 - val_loss: 1.1248
Epoch 44/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0199 - val_loss: 1.1349
Epoch 45/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0216 - val_loss: 1.1489
Epoch 46/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0243 - val_loss: 1.0859
Epoch 47/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0086 - val_loss: 1.1292
Epoch 48/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0033 - val_loss: 1.1371
Epoch 49/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0239 - val_loss: 1.0800
Epoch 50/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0055 - val_loss: 1.1027
Epoch 51/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0121 - val_loss: 1.0682
Epoch 52/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0116 - val_loss: 1.0775
Epoch 53/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0072 - val_loss: 1.0904
Epoch 54/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0115 - val_loss: 1.0718
Epoch 55/100
24/24 [==============================] - 0s 2ms/step - loss: 1.0097 - val_loss: 1.0704
Epoch 56/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9913 - val_loss: 1.0780
Epoch 57/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9942 - val_loss: 1.1146
Epoch 58/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9986 - val_loss: 1.0746
Epoch 59/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0104 - val_loss: 1.0452
Epoch 60/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9893 - val_loss: 1.1245
Epoch 61/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0022 - val_loss: 1.1223
Epoch 62/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9954 - val_loss: 1.0875
Epoch 63/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9885 - val_loss: 1.0794
Epoch 64/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9851 - val_loss: 1.0865
Epoch 65/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9913 - val_loss: 1.0807
Epoch 66/100
24/24 [==============================] - 0s 3ms/step - loss: 1.0027 - val_loss: 1.0638
Epoch 67/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9871 - val_loss: 1.0712
Epoch 68/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9815 - val_loss: 1.1130
Epoch 69/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9875 - val_loss: 1.0459
Epoch 70/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9857 - val_loss: 1.0746
Epoch 71/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9721 - val_loss: 1.0710
Epoch 72/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9717 - val_loss: 1.0511
Epoch 73/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9747 - val_loss: 1.0822
Epoch 74/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9757 - val_loss: 1.0713
Epoch 75/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9817 - val_loss: 1.0466
Epoch 76/100
24/24 [==============================] - 0s 3ms/step - loss: 0.9815 - val_loss: 1.0463
Epoch 77/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9720 - val_loss: 1.0972
Epoch 78/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9692 - val_loss: 1.0696
Epoch 79/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9780 - val_loss: 1.0752
Epoch 80/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9739 - val_loss: 1.0680
Epoch 81/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9801 - val_loss: 1.0521
Epoch 82/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9751 - val_loss: 1.0568
Epoch 83/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9712 - val_loss: 1.0740
Epoch 84/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9682 - val_loss: 1.0530
Epoch 85/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9676 - val_loss: 1.0638
Epoch 86/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9778 - val_loss: 1.0467
Epoch 87/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9667 - val_loss: 1.0449
Epoch 88/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9664 - val_loss: 1.0319
Epoch 89/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9600 - val_loss: 1.0698
Epoch 90/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9704 - val_loss: 1.0311
Epoch 91/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9741 - val_loss: 1.0587
Epoch 92/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9790 - val_loss: 1.0544
Epoch 93/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9606 - val_loss: 1.0588
Epoch 94/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9672 - val_loss: 1.0360
Epoch 95/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9629 - val_loss: 1.0227
Epoch 96/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9904 - val_loss: 1.0752
Epoch 97/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9613 - val_loss: 1.0553
Epoch 98/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9489 - val_loss: 1.0749
Epoch 99/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9623 - val_loss: 1.0403
Epoch 100/100
24/24 [==============================] - 0s 2ms/step - loss: 0.9670 - val_loss: 1.0611
In [87]:
fb_genes = ['Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6', 'Arx', 'Dlx1', 'Dlx2', 'Dlx5', 'Nr2e2', 'Otx2']
mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4']
hb_genes = ['Hoxb1', 'Krox20', 'Fev', 'Hoxd3', 'Phox2b']
sc_genes = ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11', 'Hoxa13',
            'Hoxb9', 'Hoxb13',  'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13']

ns_glutamatergic = ['Slc17a6', 'VGLUT2', 'Slc17a7', 'VGLUT1', 'Slc17a8', 'VGLUT3', 'Slc1a1', 'Slc1a2', 'Slc1a6']
ns_progenitors = ['Sox2', 'Hes1', 'Hes5', 'Vim']
prolif = ['Ccna1', 'Ccna2', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Cdc25a', 
'Cdc25b', 'Cdc25c', 'E2f1', 'E2f2', 'E2f3', 'Mcm10', 'Mcm5', 'Mcm3', 'Mcm2', 'Cip2a']

def get_idxs(genes, g_id):
    grp1_idxs = []
    i = 0
    for g in df[g_id].values:
        if g in genes:
            grp1_idxs.append(i)
        i += 1
    return grp1_idxs

from scipy import stats

def compute_dists(grp1_idxs, grp2_idxs, data):
    centroid_grp1 = np.mean(data[grp1_idxs, :], axis=0)

    grp_1_within_dist = np.sqrt(np.sum((data[grp1_idxs, :] - centroid_grp1) **2, axis=1))
    
    centroid_grp2 = np.mean(data[grp2_idxs, :], axis=0)
    grp_2_within_dist = np.sqrt(np.sum((data[grp2_idxs, :] - centroid_grp2) **2, axis=1))
    
    all_idxs = grp1_idxs + grp2_idxs
    centroid = np.mean(data[all_idxs, :], axis=0)
    between_dist = np.sqrt(np.sum((data[all_idxs, :] - centroid) **2, axis=1))
    
    t1_stat, t1_pval = stats.ttest_ind(grp_1_within_dist, between_dist)
    t2_stat, t2_pval = stats.ttest_ind(grp_2_within_dist, between_dist)
    # If both t1 and t2 are sig return that it was a success
    if t1_pval < 0.05 and t2_pval < 0.05:
        return [1, t1_pval, t2_pval, t1_stat, t2_stat]
    else:
        return [0, t1_pval, t2_pval, 0, 0]
    

def get_res(grp1, grp2, data, cond_label, data_label):
    tog = compute_dists(grp1, grp2, data)
    results_tog[cond_label][data_label] += tog[0]
    results_t[cond_label][data_label] += (abs(tog[3]) + abs(tog[4])) / 2

    # Compute it independently for each dimension
    x = 0
    for d in range(0, 3):
        res = compute_dists(grp1, grp2, data[:, d:d+1])
        x += res[0]
    # Add 1 if at least 1 dim was sig.
    results_indep[cond_label][data_label] += 1 if x > 0 else 0

results_indep = {}
results_tog = {}
results_t = {}
for c in comp:
    results_indep[c] = {}
    results_tog[c] = {}
    results_t[c] = {}
    for label, data in data_m.items():
        results_indep[c][label] = 0
        results_tog[c][label] = 0
        results_t[c][label] = 0

sc_idxs = get_idxs(sc_genes, gene_name)
fb_idxs = get_idxs(fb_genes, gene_name)
mb_idxs = get_idxs(mb_genes, gene_name)
hb_idxs = get_idxs(hb_genes, gene_name)
prolif_idxs = get_idxs(prolif, gene_name)

for data_m in data_ms:
    for label, data in data_m.items():
        # Do it together
        #grp1, grp2, data, cond_label, data_label
        get_res(sc_idxs, fb_idxs, data, 'SC and FB', label)
        get_res(sc_idxs, mb_idxs, data, 'SC and MB', label)
        get_res(sc_idxs, hb_idxs, data, 'SC and HB', label)
        get_res(sc_idxs, prolif_idxs, data, 'SC and Pro.', label)
        get_res(mb_idxs, prolif_idxs, data, 'MB and Pro.', label)
        get_res(mb_idxs, hb_idxs, data, 'MB and FB', label)
        get_res(mb_idxs, hb_idxs, data, 'MB and HB', label)
        get_res(hb_idxs, fb_idxs, data, 'HB and FB', label)
        get_res(hb_idxs, prolif_idxs, data, 'HB and Pro.', label)
        get_res(fb_idxs, prolif_idxs, data, 'FB and Pro.', label)
In [95]:
for test_label, values in results_tog.items():
    v_df = pd.DataFrame()
    x = []
    y = []
    
    for run_lbl, value in values.items():
        if 'PCA' not in run_lbl and 'PHATE' not in run_lbl:
            if run_lbl == 'VAE-lin':
                x.append('VAE shallow')
            elif run_lbl == 'VAE-deep':
                x.append('VAE deep')
            elif run_lbl == 'TSNE':
                x.append('tSNE')
            else:
                x.append(run_lbl)

            if test_label == 'FB and Pro.' or test_label == 'SC and HB' or test_label == 'MB and Pro.' or test_label == 'MB and FB' or test_label == 'MB and HB':
                y.append(100 - ((value/30) * 100))
            else:
                y.append((value/30) * 100)
    v_df['x'] = x
    v_df['y'] = y
    bc = Barchart(v_df, 'x', 'y',title=test_label, order=['VAE deep', 'VAE shallow', 'UMAP', 'tSNE'])
    bc.plot()
    save_fig(f'Bar_{test_label}')
    plt.show()
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
In [94]:
###############################################################################
#                                                                             #
#    This program is free software: you can redistribute it and/or modify     #
#    it under the terms of the GNU General Public License as published by     #
#    the Free Software Foundation, either version 3 of the License, or        #
#    (at your option) any later version.                                      #
#                                                                             #
#    This program is distributed in the hope that it will be useful,          #
#    but WITHOUT ANY WARRANTY; without even the implied warranty of           #
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the            #
#    GNU General Public License for more details.                             #
#                                                                             #
#    You should have received a copy of the GNU General Public License        #
#    along with this program. If not, see <http://www.gnu.org/licenses/>.     #
#                                                                             #
###############################################################################

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

from sciviso import Vis


class Barchart(Vis):

    def __init__(self, df: pd.DataFrame, x: object, y: object, title='', xlabel='', ylabel='', hue=None, order=None,
                 hue_order=None, figsize=(1.0, 1.0), title_font_size=8, label_font_size=6, title_font_weight=700,
                 errwidth=0, linewidth=1, edgecolor="k"):
        super().__init__(df, figsize=figsize, title_font_size=title_font_size, label_font_size=label_font_size,
                         title_font_weight=title_font_weight)
        super().__init__(df)
        self.df = df
        self.x = x
        self.y = y
        self.title = title
        self.hue = hue
        self.order = order
        self.hue_order = hue_order
        self.label = 'barchart'
        self.xlabel = xlabel
        self.ylabel = ylabel
        self.errwidth = errwidth
        self.linewidth =linewidth
        self.edgecolor = edgecolor
        # '#483873', '#1BD8A6', '#B117B7',
        sci_colour = ['#FFC107', '#016957', '#9785C0', 
             '#D09139', '#338A03', '#FF69A1', '#5930B1', '#FFE884', '#35B567', '#1E88E5', 
             '#ACAD60', '#A2FFB4', '#B618F5']
        self.palette = sns.color_palette(sci_colour)
        
    def plot(self):
        x, y, hue_order, order, hue = self.x, self.y, self.hue_order, self.order, self.hue
        plt.rcParams['figure.figsize'] = (1.0, 1.5)
        sns.set(rc={'figure.figsize': (1.0, 1.5), 'font.family': 'sans-serif',
                    'font.sans-serif': 'Arial', 'font.size': 6}, style=self.style)
        # First lets check whether we were passed lists or strings for our y and x arrays
        if not isinstance(x, str) and not isinstance(y, str):
            vis_df = pd.DataFrame()
            vis_df['x'] = x
            vis_df['y'] = y
            x = 'x'
            y = 'y'
            if hue is not None:
                vis_df['colour'] = hue
                hue = 'colour'
            if order is None:
                order = list(set(vis_df['x'].values))
                order.sort()
        else:
            vis_df = self.df
        # set the orders
        if hue_order is None and hue is not None:
            hue_order = list(set(vis_df[hue].values))
            hue_order.sort()
        if order is None:
            order = list(set(vis_df[x].values))
            order.sort()

        ax = sns.barplot(data=vis_df, x=x, y=y, hue=hue, hue_order=hue_order, order=order, palette=self.palette,
                          linewidth=self.linewidth, alpha=0.8, errwidth=self.errwidth)
        ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')
        plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=self.label_font_size)
        ax.tick_params(labelsize=self.label_font_size)
        ax.set_ylim(0, 100)
        self.add_labels()
        self.set_ax_params(ax)
        return ax
    
    
    def set_ax_params(self, ax):
        ax.tick_params(direction='out', length=2, width=0.5)
        ax.spines['bottom'].set_linewidth(0.5)
        ax.spines['top'].set_linewidth(0)
        ax.spines['left'].set_linewidth(0.5)
        ax.spines['right'].set_linewidth(0)
        ax.set_xticklabels(ax.get_xticklabels(), weight = 'bold')
        ax.tick_params(labelsize=self.label_font_size)
        ax.tick_params(axis='x', which='major', pad=2.0)
        ax.tick_params(axis='y', which='major', pad=2.0)
In [98]:
gene_markers_sep = [prolif, fb_genes, mb_genes, hb_genes, sc_genes]
marker_labels_sep = ['Proliferation', 'forebrain', 'midbrain', 'hindbrain',  'spinalcord']

color_map = {}
i = 6
for c in marker_labels_sep:
    if 'brain' in c or 'spinal' in c:
        if 'spinal' in c:
            color_map[c] = sc_colour
        else:
            color_map[c] = get_tissue_colour(c.lower())
    else:
        color_map[c] = sci_colour[i]
    i += 1
def sig_dist_bp(grp1_idxs, grp2_idxs, data, grp1_label, grp2_label, title, box_colors):

    centroid_grp1 = np.mean(data[grp1_idxs, :], axis=0)

    grp_1_within_dist = np.sqrt(np.sum((data[grp1_idxs, :] - centroid_grp1) **2, axis=1))
    
    centroid_grp2 = np.mean(data[grp2_idxs, :], axis=0)
    grp_2_within_dist = np.sqrt(np.sum((data[grp2_idxs, :] - centroid_grp2) **2, axis=1))
    
    all_idxs = grp1_idxs + grp2_idxs
    centroid = np.mean(data[all_idxs, :], axis=0)
    between_dist = np.sqrt(np.sum((data[all_idxs, :] - centroid) **2, axis=1))
    
    box_df = pd.DataFrame()
    all_label = f'{grp1_label.split(" ")[0]} & {grp2_label}'
    box_df['Conditions'] = [grp1_label]*len(grp_1_within_dist) + [grp2_label]*len(grp_2_within_dist) +  [all_label]*len(between_dist)
    box_df['Values'] = list(grp_1_within_dist) + list(grp_2_within_dist) + list(between_dist)
    dists = np.array(box_df['Values'].values)
    labels = np.array(box_df['Conditions'].values)
    
    print(f'---------------- {title} {grp1_label} {grp2_label} --------------------')
    print(f'{grp1_label} vs {grp2_label} {stats.ttest_ind(dists[np.where(labels == grp1_label)], dists[np.where(labels == grp2_label)])}')
    print(f'{grp1_label} vs {grp1_label}-{grp2_label} {stats.ttest_ind(dists[np.where(labels == grp1_label)], dists[np.where(labels == all_label)])}')
    print(f'{grp2_label} vs {grp1_label}-{grp2_label} {stats.ttest_ind(dists[np.where(labels == grp2_label)], dists[np.where(labels == all_label)])}')
    print('----------------------------------------')
    boxplot = Boxplot(box_df, "Conditions", "Values", title=f'{title} {grp1_label} {grp2_label}', add_stats=True, add_dots=True, 
                      order=[grp1_label, grp2_label, f'{grp1_label.split(" ")[0]} & {grp2_label}'], box_colors=box_colors)
    boxplot.palette = sci_colour
    boxplot.stat_method = 't-test_ind'
    boxplot.plot()
    save_fig(f't-test_{title}-{grp1_label}-{grp2_label}')
    plt.show()

for label, data in data_ms[0].items():
    # plot the first one on a scatter plot for visualisation purposes
    vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
                                    gene_markers_sep, output_dir=fig_dir, plt_bg=False, vae_data=data, 
                                    fig_type="svg",
                                    title=f'Markers sep {label} {experiment_name}',
                                    show_plt=False, save_fig=False, color_map=color_map, angle_plot=90)
    plt.show()
    pro_colour = '#9785C0'
    sig_dist_bp(sc_idxs, fb_idxs, data_m[label], 'SC', 'FB', label, [fb_color, sc_color, grey])
    sig_dist_bp(fb_idxs, mb_idxs, data_m[label], 'FB', 'MB', label, [fb_color, mb_color, grey])
    sig_dist_bp(fb_idxs, hb_idxs, data_m[label], 'FB', 'HB', label, [fb_color, hb_color, grey])
    sig_dist_bp(mb_idxs, sc_idxs, data_m[label], 'MB', 'SC', label, [mb_color, sc_color, grey])
    sig_dist_bp(mb_idxs, hb_idxs, data_m[label], 'MB', 'HB', label, [fb_color, sc_color, grey])
    sig_dist_bp(hb_idxs, sc_idxs, data_m[label], 'HB', 'SC', label, [hb_color, sc_color, grey])
    
    sig_dist_bp(fb_idxs, prolif_idxs, data_m[label], 'FB', 'Pro.', label, [fb_color, pro_colour, grey])
    sig_dist_bp(mb_idxs, prolif_idxs, data_m[label], 'MB', 'Pro.', label, [mb_color, pro_colour, grey])
    sig_dist_bp(hb_idxs, prolif_idxs, data_m[label], 'HB', 'Pro.', label, [hb_color, pro_colour, grey])
    sig_dist_bp(sc_idxs, prolif_idxs, data_m[label], 'SC', 'Pro.', label, [sc_color, pro_colour, grey])
    
<Figure size 144x144 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 22.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 17.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
---------------- VAE-deep SC FB --------------------
SC vs FB Ttest_indResult(statistic=-0.5713675335213038, pvalue=0.5726570755195275)
SC vs SC-FB Ttest_indResult(statistic=-6.317909401974255, pvalue=1.1497434208678654e-07)
FB vs SC-FB Ttest_indResult(statistic=-4.293909796214078, pvalue=0.00012677489151328202)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-5.714e-01
FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=3.803e-04 stat=-4.294e+00
SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=3.449e-07 stat=-6.318e+00
---------------- VAE-deep FB MB --------------------
FB vs MB Ttest_indResult(statistic=-0.49966695105948045, pvalue=0.6256594987116564)
FB vs FB-MB Ttest_indResult(statistic=-1.7900627589619738, pvalue=0.08661898592385908)
MB vs FB-MB Ttest_indResult(statistic=-0.8472078867523739, pvalue=0.4080031587961723)
----------------------------------------
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-4.997e-01
MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.472e-01
FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=2.599e-01 stat=-1.790e+00
---------------- VAE-deep FB HB --------------------
FB vs HB Ttest_indResult(statistic=-1.2841001434169819, pvalue=0.22549379087580165)
FB vs FB-HB Ttest_indResult(statistic=-2.4735984449126707, pvalue=0.021995520628986995)
HB vs FB-HB Ttest_indResult(statistic=-0.7473386372859272, pvalue=0.4672266262028567)
----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 7.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=6.765e-01 stat=-1.284e+00
HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-7.473e-01
FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=6.599e-02 stat=-2.474e+00
---------------- VAE-deep MB SC --------------------
MB vs SC Ttest_indResult(statistic=1.1108400058608072, pvalue=0.27919464911071834)
MB vs MB-SC Ttest_indResult(statistic=-0.4925108252655286, pvalue=0.6264917360781916)
SC vs MB-SC Ttest_indResult(statistic=-2.370189683839991, pvalue=0.022819640514216484)
----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=8.376e-01 stat=1.111e+00
SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=6.846e-02 stat=-2.370e+00
MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-4.925e-01
---------------- VAE-deep MB HB --------------------
MB vs HB Ttest_indResult(statistic=-0.6655294890549696, pvalue=0.5304496528520609)
MB vs MB-HB Ttest_indResult(statistic=-1.101265081141572, pvalue=0.2942952435208552)
HB vs MB-HB Ttest_indResult(statistic=-0.36773302429350285, pvalue=0.7215716323963325)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-6.655e-01
No handles with labels found to put in legend.
HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-3.677e-01
MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=8.829e-01 stat=-1.101e+00
---------------- VAE-deep HB SC --------------------
HB vs SC Ttest_indResult(statistic=2.117440753030659, pvalue=0.04763742220145131)
HB vs HB-SC Ttest_indResult(statistic=0.9648453482803243, pvalue=0.34510923411352423)
SC vs HB-SC Ttest_indResult(statistic=-1.2887910216341856, pvalue=0.20547094764501697)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=1.429e-01 stat=2.117e+00
No handles with labels found to put in legend.
SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=6.164e-01 stat=-1.289e+00
HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=9.648e-01
---------------- VAE-deep FB Pro. --------------------
FB vs Pro. Ttest_indResult(statistic=-0.7160805703589352, pvalue=0.48571525702050267)
FB vs FB-Pro. Ttest_indResult(statistic=-3.128241889349353, pvalue=0.004566886906921226)
Pro. vs FB-Pro. Ttest_indResult(statistic=-1.6059309207570986, pvalue=0.12396339999816983)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-7.161e-01
No handles with labels found to put in legend.
Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=3.719e-01 stat=-1.606e+00
FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.370e-02 stat=-3.128e+00
---------------- VAE-deep MB Pro. --------------------
MB vs Pro. Ttest_indResult(statistic=-0.1831504987497843, pvalue=0.8587394792330867)
MB vs MB-Pro. Ttest_indResult(statistic=-2.959421603925317, pvalue=0.010351023146649053)
Pro. vs MB-Pro. Ttest_indResult(statistic=-2.394047549157817, pvalue=0.030173833843288384)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-1.832e-01
No handles with labels found to put in legend.
Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=9.052e-02 stat=-2.394e+00
MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=3.105e-02 stat=-2.959e+00
---------------- VAE-deep HB Pro. --------------------
HB vs Pro. Ttest_indResult(statistic=0.4002407532599962, pvalue=0.7009123231136045)
HB vs HB-Pro. Ttest_indResult(statistic=-2.137440472145789, pvalue=0.05828590200696136)
Pro. vs HB-Pro. Ttest_indResult(statistic=-3.0037855778404245, pvalue=0.010164629187903201)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=4.002e-01
No handles with labels found to put in legend.
Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=3.049e-02 stat=-3.004e+00
HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.749e-01 stat=-2.137e+00
---------------- VAE-deep SC Pro. --------------------
SC vs Pro. Ttest_indResult(statistic=-1.3813286899158557, pvalue=0.18104775223443362)
SC vs SC-Pro. Ttest_indResult(statistic=-4.773858499297587, pvalue=2.4270270890799944e-05)
Pro. vs SC-Pro. Ttest_indResult(statistic=-2.0220128837994475, pvalue=0.05282374576857514)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=5.431e-01 stat=-1.381e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 22.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 20.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=1.585e-01 stat=-2.022e+00
SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=7.281e-05 stat=-4.774e+00
<Figure size 108x108 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 10.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
---------------- VAE-lin SC FB --------------------
SC vs FB Ttest_indResult(statistic=0.31518122558203204, pvalue=0.7551398475903303)
SC vs SC-FB Ttest_indResult(statistic=-6.191114936576155, pvalue=1.7668093370475303e-07)
FB vs SC-FB Ttest_indResult(statistic=-5.278801083583145, pvalue=6.393827112328868e-06)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=3.152e-01
FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=1.918e-05 stat=-5.279e+00
SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=5.300e-07 stat=-6.191e+00
---------------- VAE-lin FB MB --------------------
FB vs MB Ttest_indResult(statistic=-1.7676205332113522, pvalue=0.10057266265664161)
FB vs FB-MB Ttest_indResult(statistic=-1.3592155680768048, pvalue=0.18725757385663316)
MB vs FB-MB Ttest_indResult(statistic=0.7501788919196046, pvalue=0.46284082922738234)
----------------------------------------
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=3.017e-01 stat=-1.768e+00
MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=7.502e-01
FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=5.618e-01 stat=-1.359e+00
---------------- VAE-lin FB HB --------------------
FB vs HB Ttest_indResult(statistic=-2.9638707217470106, pvalue=0.012885794610249036)
FB vs FB-HB Ttest_indResult(statistic=-1.5010397530486406, pvalue=0.1482321925235325)
HB vs FB-HB Ttest_indResult(statistic=0.48859656617942476, pvalue=0.6326919249140546)
----------------------------------------
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=3.866e-02 stat=-2.964e+00
HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=4.886e-01
FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=4.447e-01 stat=-1.501e+00
---------------- VAE-lin MB SC --------------------
MB vs SC Ttest_indResult(statistic=1.5604161620887171, pvalue=0.13360593804720589)
MB vs MB-SC Ttest_indResult(statistic=-0.4461763488138625, pvalue=0.6591636616901317)
SC vs MB-SC Ttest_indResult(statistic=-2.588862884872793, pvalue=0.013464097247586496)
----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 22.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 17.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=4.008e-01 stat=1.560e+00
SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=4.039e-02 stat=-2.589e+00
MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-4.462e-01
---------------- VAE-lin MB HB --------------------
MB vs HB Ttest_indResult(statistic=-0.29663524118743556, pvalue=0.7767454633531031)
MB vs MB-HB Ttest_indResult(statistic=-0.3412727412905872, pvalue=0.7393281096402708)
HB vs MB-HB Ttest_indResult(statistic=-0.028241801348878243, pvalue=0.9780856271675468)
----------------------------------------
p-value annotation legend:
No handles with labels found to put in legend.
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-2.966e-01
HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-2.824e-02
MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-3.413e-01
---------------- VAE-lin HB SC --------------------
HB vs SC Ttest_indResult(statistic=1.987459337768919, pvalue=0.06147959412993316)
HB vs HB-SC Ttest_indResult(statistic=1.0202404073409508, pvalue=0.31870340195510427)
SC vs HB-SC Ttest_indResult(statistic=-1.2032143277810325, pvalue=0.23653590501889332)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=1.844e-01 stat=1.987e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=7.096e-01 stat=-1.203e+00
HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=9.561e-01 stat=1.020e+00
---------------- VAE-lin FB Pro. --------------------
FB vs Pro. Ttest_indResult(statistic=-0.9830311832466065, pvalue=0.3422764287757093)
FB vs FB-Pro. Ttest_indResult(statistic=-4.431071376286962, pvalue=0.0001764966505602265)
Pro. vs FB-Pro. Ttest_indResult(statistic=-1.9913815620472537, pvalue=0.06027435884898085)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-9.830e-01
No handles with labels found to put in legend.
Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.808e-01 stat=-1.991e+00
FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=5.295e-04 stat=-4.431e+00
---------------- VAE-lin MB Pro. --------------------
MB vs Pro. Ttest_indResult(statistic=0.37165511926590805, pvalue=0.7187487640799695)
MB vs MB-Pro. Ttest_indResult(statistic=-3.3509066725898244, pvalue=0.004755379296821484)
Pro. vs MB-Pro. Ttest_indResult(statistic=-3.791661386411261, pvalue=0.001773204894323304)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=3.717e-01
No handles with labels found to put in legend.
Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=5.320e-03 stat=-3.792e+00
MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.427e-02 stat=-3.351e+00
---------------- VAE-lin HB Pro. --------------------
HB vs Pro. Ttest_indResult(statistic=0.612778249538278, pvalue=0.559396468260031)
HB vs HB-Pro. Ttest_indResult(statistic=-1.8724583145599187, pvalue=0.09064098162800363)
Pro. vs HB-Pro. Ttest_indResult(statistic=-2.918891228441536, pvalue=0.01196595614488854)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=6.128e-01
No handles with labels found to put in legend.
Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=3.590e-02 stat=-2.919e+00
HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.719e-01 stat=-1.872e+00
---------------- VAE-lin SC Pro. --------------------
SC vs Pro. Ttest_indResult(statistic=-0.8642697533903679, pvalue=0.39676551858013487)
SC vs SC-Pro. Ttest_indResult(statistic=-4.88123346681544, pvalue=1.72742162261532e-05)
Pro. vs SC-Pro. Ttest_indResult(statistic=-2.3717852046112213, pvalue=0.02481398036542446)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.643e-01
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 22.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 25.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=7.444e-02 stat=-2.372e+00
SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=5.182e-05 stat=-4.881e+00
<Figure size 108x108 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 22.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 10.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
---------------- PCA SC FB --------------------
SC vs FB Ttest_indResult(statistic=-1.0351485463324819, pvalue=0.31013207683697863)
SC vs SC-FB Ttest_indResult(statistic=-5.491464977438773, pvalue=1.8749347427738307e-06)
FB vs SC-FB Ttest_indResult(statistic=-3.4746814554549617, pvalue=0.0013510358921303058)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=9.304e-01 stat=-1.035e+00
FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=4.053e-03 stat=-3.475e+00
SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=5.625e-06 stat=-5.491e+00
---------------- PCA FB MB --------------------
FB vs MB Ttest_indResult(statistic=-0.9687174189795624, pvalue=0.35036447252340885)
FB vs FB-MB Ttest_indResult(statistic=-0.8088195484852897, pvalue=0.4269068361365953)
MB vs FB-MB Ttest_indResult(statistic=0.4126143533608857, pvalue=0.6847621936569993)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-9.687e-01
No handles with labels found to put in legend.
MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=4.126e-01
FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.088e-01
---------------- PCA FB HB --------------------
FB vs HB Ttest_indResult(statistic=-2.155564782242825, pvalue=0.05412045516846417)
FB vs FB-HB Ttest_indResult(statistic=-1.1897384119086292, pvalue=0.24742695714511814)
HB vs FB-HB Ttest_indResult(statistic=0.6327899200268615, pvalue=0.5370702250941283)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.624e-01 stat=-2.156e+00
No handles with labels found to put in legend.
HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=6.328e-01
FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=7.423e-01 stat=-1.190e+00
---------------- PCA MB SC --------------------
MB vs SC Ttest_indResult(statistic=1.8476333644097442, pvalue=0.07879158866825213)
MB vs MB-SC Ttest_indResult(statistic=-0.11864139758875361, pvalue=0.906471361686173)
SC vs MB-SC Ttest_indResult(statistic=-2.4159738930460484, pvalue=0.020475690408691797)
----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 22.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 13.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=2.364e-01 stat=1.848e+00
SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=6.143e-02 stat=-2.416e+00
MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-1.186e-01
---------------- PCA MB HB --------------------
MB vs HB Ttest_indResult(statistic=-0.806024140790998, pvalue=0.45098442021638274)
MB vs MB-HB Ttest_indResult(statistic=-0.690863003361627, pvalue=0.5039696025260364)
HB vs MB-HB Ttest_indResult(statistic=0.17126356118190222, pvalue=0.8678052484882)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.060e-01
No handles with labels found to put in legend.
HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=1.713e-01
MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-6.909e-01
---------------- PCA HB SC --------------------
HB vs SC Ttest_indResult(statistic=3.0348065619000524, pvalue=0.006815050733874745)
HB vs HB-SC Ttest_indResult(statistic=1.9321748666012766, pvalue=0.06631660535264444)
SC vs HB-SC Ttest_indResult(statistic=-1.1317651736411656, pvalue=0.2650151673798653)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=2.045e-02 stat=3.035e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=7.950e-01 stat=-1.132e+00
HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=1.989e-01 stat=1.932e+00
---------------- PCA FB Pro. --------------------
FB vs Pro. Ttest_indResult(statistic=-1.7551077251773144, pvalue=0.10108868047613696)
FB vs FB-Pro. Ttest_indResult(statistic=-4.028832847288123, pvalue=0.000489871576016114)
Pro. vs FB-Pro. Ttest_indResult(statistic=-1.1777269669236932, pvalue=0.2527302925792264)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=3.033e-01 stat=-1.755e+00
No handles with labels found to put in legend.
Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=7.582e-01 stat=-1.178e+00
FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.470e-03 stat=-4.029e+00
---------------- PCA MB Pro. --------------------
MB vs Pro. Ttest_indResult(statistic=-0.8007507603917966, pvalue=0.4438998613417492)
MB vs MB-Pro. Ttest_indResult(statistic=-5.083647483547039, pvalue=0.00016664056504065342)
Pro. vs MB-Pro. Ttest_indResult(statistic=-2.4692834559270582, pvalue=0.02603524260358536)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.008e-01
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 9.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=7.811e-02 stat=-2.469e+00
MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=4.999e-04 stat=-5.084e+00
---------------- PCA HB Pro. --------------------
HB vs Pro. Ttest_indResult(statistic=-0.19559492696448114, pvalue=0.8504837979554903)
HB vs HB-Pro. Ttest_indResult(statistic=-2.5787366154958438, pvalue=0.02747579102232782)
Pro. vs HB-Pro. Ttest_indResult(statistic=-2.4539033762445617, pvalue=0.028999464650394314)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-1.956e-01
No handles with labels found to put in legend.
Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=8.700e-02 stat=-2.454e+00
HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=8.243e-02 stat=-2.579e+00
---------------- PCA SC Pro. --------------------
SC vs Pro. Ttest_indResult(statistic=-2.7554249618815176, pvalue=0.01154416706972998)
SC vs SC-Pro. Ttest_indResult(statistic=-5.019886005920669, pvalue=1.1112741930933355e-05)
Pro. vs SC-Pro. Ttest_indResult(statistic=-1.42661554778421, pvalue=0.1647469983873798)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=3.463e-02 stat=-2.755e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 25.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=4.942e-01 stat=-1.427e+00
SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=3.334e-05 stat=-5.020e+00
<Figure size 108x108 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 22.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 25.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
---------------- UMAP SC FB --------------------
SC vs FB Ttest_indResult(statistic=1.491004069658413, pvalue=0.1479941921032804)
SC vs SC-FB Ttest_indResult(statistic=-10.008755424355657, pvalue=6.503129429896634e-13)
FB vs SC-FB Ttest_indResult(statistic=-9.05091653058277, pvalue=8.315935942588408e-11)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=4.440e-01 stat=1.491e+00
FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=2.495e-10 stat=-9.051e+00
SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=1.951e-12 stat=-1.001e+01
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
---------------- UMAP FB MB --------------------
FB vs MB Ttest_indResult(statistic=-4.7002940437730185, pvalue=0.00041499709563661864)
FB vs FB-MB Ttest_indResult(statistic=-4.698049520956051, pvalue=9.87965251963714e-05)
MB vs FB-MB Ttest_indResult(statistic=-0.9444515327130983, pvalue=0.3574448213803696)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=1.245e-03 stat=-4.700e+00
MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-9.445e-01
FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=2.964e-04 stat=-4.698e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 23.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
---------------- UMAP FB HB --------------------
FB vs HB Ttest_indResult(statistic=-5.080872222526071, pvalue=0.00035449642720414715)
FB vs FB-HB Ttest_indResult(statistic=-2.84305336775656, pvalue=0.009740231874551393)
HB vs FB-HB Ttest_indResult(statistic=1.032888693128249, pvalue=0.31916985019186883)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.063e-03 stat=-5.081e+00
HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=9.575e-01 stat=1.033e+00
FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=2.922e-02 stat=-2.843e+00
---------------- UMAP MB SC --------------------
MB vs SC Ttest_indResult(statistic=2.719489361680557, pvalue=0.012842137941889337)
MB vs MB-SC Ttest_indResult(statistic=-1.4395307801261599, pvalue=0.16193242478776962)
SC vs MB-SC Ttest_indResult(statistic=-4.456451082240254, pvalue=6.838728946570474e-05)
----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 39.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=3.853e-02 stat=2.719e+00
SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=2.052e-04 stat=-4.456e+00
MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=4.858e-01 stat=-1.440e+00
---------------- UMAP MB HB --------------------
MB vs HB Ttest_indResult(statistic=-2.263473500945587, pvalue=0.06424090341270594)
MB vs MB-HB Ttest_indResult(statistic=-1.7676118769528375, pvalue=0.10481688842387107)
HB vs MB-HB Ttest_indResult(statistic=0.3163087946929721, pvalue=0.7589811514442426)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.927e-01 stat=-2.263e+00
No handles with labels found to put in legend.
HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=3.163e-01
MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=3.145e-01 stat=-1.768e+00
---------------- UMAP HB SC --------------------
HB vs SC Ttest_indResult(statistic=5.266141794956731, pvalue=4.4026814533067997e-05)
HB vs HB-SC Ttest_indResult(statistic=1.924559523453333, pvalue=0.06731365117951155)
SC vs HB-SC Ttest_indResult(statistic=-2.0786394980740632, pvalue=0.04463994902730383)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=1.321e-04 stat=5.266e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 28.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=1.339e-01 stat=-2.079e+00
HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=2.019e-01 stat=1.925e+00
---------------- UMAP FB Pro. --------------------
FB vs Pro. Ttest_indResult(statistic=-3.7003803497881504, pvalue=0.002375531065733825)
FB vs FB-Pro. Ttest_indResult(statistic=-7.7623133555403685, pvalue=5.3599339575450594e-08)
Pro. vs FB-Pro. Ttest_indResult(statistic=-3.060677640145886, pvalue=0.006170584842282441)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=7.127e-03 stat=-3.700e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 40.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 25.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.851e-02 stat=-3.061e+00
FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.608e-07 stat=-7.762e+00
---------------- UMAP MB Pro. --------------------
MB vs Pro. Ttest_indResult(statistic=-1.8722817835636727, pvalue=0.09395612236550313)
MB vs MB-Pro. Ttest_indResult(statistic=-7.016473894251053, pvalue=6.087279693373256e-06)
Pro. vs MB-Pro. Ttest_indResult(statistic=-3.6471407404937635, pvalue=0.002383773950460339)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=2.819e-01 stat=-1.872e+00
No handles with labels found to put in legend.
Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=7.151e-03 stat=-3.647e+00
MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.826e-05 stat=-7.016e+00
---------------- UMAP HB Pro. --------------------
HB vs Pro. Ttest_indResult(statistic=-0.44093301813412894, pvalue=0.6725583468397334)
HB vs HB-Pro. Ttest_indResult(statistic=-3.0453866011462023, pvalue=0.012348537942347745)
Pro. vs HB-Pro. Ttest_indResult(statistic=-3.3113786029353007, pvalue=0.005623266309307763)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-4.409e-01
No handles with labels found to put in legend.
Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.687e-02 stat=-3.311e+00
HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=3.705e-02 stat=-3.045e+00
---------------- UMAP SC Pro. --------------------
SC vs Pro. Ttest_indResult(statistic=-4.551939000629979, pvalue=0.0001566872706929742)
SC vs SC-Pro. Ttest_indResult(statistic=-6.019109258759003, pvalue=4.4428865079380856e-07)
Pro. vs SC-Pro. Ttest_indResult(statistic=-1.9566898934538954, pvalue=0.060423688733640804)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=4.701e-04 stat=-4.552e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 61.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 54.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=1.813e-01 stat=-1.957e+00
SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=1.333e-06 stat=-6.019e+00
<Figure size 108x108 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 27.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 28.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
---------------- PHATE SC FB --------------------
SC vs FB Ttest_indResult(statistic=0.49334665898961094, pvalue=0.6259091662054275)
SC vs SC-FB Ttest_indResult(statistic=-6.667562712890706, pvalue=3.516836706934543e-08)
FB vs SC-FB Ttest_indResult(statistic=-5.3371777676401635, pvalue=5.3440536233698165e-06)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=4.933e-01
FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=1.603e-05 stat=-5.337e+00
SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=1.055e-07 stat=-6.668e+00
No handles with labels found to put in legend.
---------------- PHATE FB MB --------------------
FB vs MB Ttest_indResult(statistic=-0.8861094508259468, pvalue=0.39165565463554897)
FB vs FB-MB Ttest_indResult(statistic=-0.6439597738070608, pvalue=0.5259716860728341)
MB vs FB-MB Ttest_indResult(statistic=0.4511777926095601, pvalue=0.6572497405204243)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.861e-01
MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=4.512e-01
FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-6.440e-01
---------------- PHATE FB HB --------------------
FB vs HB Ttest_indResult(statistic=-2.3466921540681662, pvalue=0.038712583858871066)
FB vs FB-HB Ttest_indResult(statistic=-1.226754710819689, pvalue=0.2334917563587243)
HB vs FB-HB Ttest_indResult(statistic=0.7975384793439153, pvalue=0.43845673696511345)
----------------------------------------
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.161e-01 stat=-2.347e+00
HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=7.975e-01
FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=7.005e-01 stat=-1.227e+00
---------------- PHATE MB SC --------------------
MB vs SC Ttest_indResult(statistic=0.7419494211885903, pvalue=0.4663358900995771)
MB vs MB-SC Ttest_indResult(statistic=-1.7643158133892738, pvalue=0.08942627840851332)
SC vs MB-SC Ttest_indResult(statistic=-3.792022955662992, pvalue=0.0005072344445517199)
----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 22.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=7.419e-01
SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=1.522e-03 stat=-3.792e+00
MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=2.683e-01 stat=-1.764e+00
---------------- PHATE MB HB --------------------
MB vs HB Ttest_indResult(statistic=-2.465687521147141, pvalue=0.0487419345505173)
MB vs MB-HB Ttest_indResult(statistic=-1.7714223778661353, pvalue=0.10415566341264018)
HB vs MB-HB Ttest_indResult(statistic=0.08345557109741318, pvalue=0.9353160698125261)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.462e-01 stat=-2.466e+00
No handles with labels found to put in legend.
HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=8.346e-02
MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=3.125e-01 stat=-1.771e+00
---------------- PHATE HB SC --------------------
HB vs SC Ttest_indResult(statistic=2.868446082538814, pvalue=0.00983677941731196)
HB vs HB-SC Ttest_indResult(statistic=1.1508040677813591, pvalue=0.26216784556351425)
SC vs HB-SC Ttest_indResult(statistic=-1.600409078362103, pvalue=0.11801299973661102)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=2.951e-02 stat=2.868e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=3.540e-01 stat=-1.600e+00
HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=7.865e-01 stat=1.151e+00
---------------- PHATE FB Pro. --------------------
FB vs Pro. Ttest_indResult(statistic=-3.0369212636491154, pvalue=0.008877195621999708)
FB vs FB-Pro. Ttest_indResult(statistic=-5.840353417802443, pvalue=5.051822724266418e-06)
Pro. vs FB-Pro. Ttest_indResult(statistic=-1.8808182787969554, pvalue=0.07463841050923263)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=2.663e-02 stat=-3.037e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.239e-01 stat=-1.881e+00
FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.516e-05 stat=-5.840e+00
---------------- PHATE MB Pro. --------------------
MB vs Pro. Ttest_indResult(statistic=-2.025798776247415, pvalue=0.0734311210612961)
MB vs MB-Pro. Ttest_indResult(statistic=-6.623570490648143, pvalue=1.1444266134693333e-05)
Pro. vs MB-Pro. Ttest_indResult(statistic=-3.244848055290337, pvalue=0.005440436013622034)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=2.203e-01 stat=-2.026e+00
No handles with labels found to put in legend.
Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.632e-02 stat=-3.245e+00
MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=3.433e-05 stat=-6.624e+00
---------------- PHATE HB Pro. --------------------
HB vs Pro. Ttest_indResult(statistic=-0.5265811439820354, pvalue=0.6147523677410285)
HB vs HB-Pro. Ttest_indResult(statistic=-3.0441094068477637, pvalue=0.012375481033803573)
Pro. vs HB-Pro. Ttest_indResult(statistic=-3.0425251888660005, pvalue=0.009434699439818341)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-5.266e-01
No handles with labels found to put in legend.
Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.830e-02 stat=-3.043e+00
HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=3.713e-02 stat=-3.044e+00
---------------- PHATE SC Pro. --------------------
SC vs Pro. Ttest_indResult(statistic=-3.8227473187309244, pvalue=0.0009283825340826467)
SC vs SC-Pro. Ttest_indResult(statistic=-5.254661969039952, pvalue=5.242506649712258e-06)
Pro. vs SC-Pro. Ttest_indResult(statistic=-1.4894225662026237, pvalue=0.14755482293046593)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=2.785e-03 stat=-3.823e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 29.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=4.427e-01 stat=-1.489e+00
SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=1.573e-05 stat=-5.255e+00
<Figure size 108x108 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 38.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 32.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
---------------- TSNE SC FB --------------------
SC vs FB Ttest_indResult(statistic=0.16861805395955629, pvalue=0.86740198592114)
SC vs SC-FB Ttest_indResult(statistic=-8.968140872668661, pvalue=1.7189787644907824e-11)
FB vs SC-FB Ttest_indResult(statistic=-7.069701846188541, pvalue=2.6601656706714162e-08)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=1.686e-01
FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=7.980e-08 stat=-7.070e+00
SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=5.157e-11 stat=-8.968e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 6.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
---------------- TSNE FB MB --------------------
FB vs MB Ttest_indResult(statistic=-4.595202770264406, pvalue=0.0005020579544925852)
FB vs FB-MB Ttest_indResult(statistic=-4.425370456358105, pvalue=0.00019499198729990206)
MB vs FB-MB Ttest_indResult(statistic=-0.9977043022654051, pvalue=0.33164699749568083)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=1.506e-03 stat=-4.595e+00
MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=9.949e-01 stat=-9.977e-01
FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=5.850e-04 stat=-4.425e+00
---------------- TSNE FB HB --------------------
FB vs HB Ttest_indResult(statistic=-4.528349905390498, pvalue=0.0008601599684373129)
FB vs FB-HB Ttest_indResult(statistic=-2.934651156766874, pvalue=0.007917258029133855)
HB vs FB-HB Ttest_indResult(statistic=0.2410356199800425, pvalue=0.813021739806644)
----------------------------------------
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=2.580e-03 stat=-4.528e+00
HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=2.410e-01
FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=2.375e-02 stat=-2.935e+00
---------------- TSNE MB SC --------------------
MB vs SC Ttest_indResult(statistic=4.845085238485979, pvalue=8.647974314393708e-05)
MB vs MB-SC Ttest_indResult(statistic=-0.4886965676351822, pvalue=0.6291533733873849)
SC vs MB-SC Ttest_indResult(statistic=-4.036701868852685, pvalue=0.00024535534145398)
----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 38.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 30.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=2.594e-04 stat=4.845e+00
SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=7.361e-04 stat=-4.037e+00
MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-4.887e-01
---------------- TSNE MB HB --------------------
MB vs HB Ttest_indResult(statistic=-1.3593967138465326, pvalue=0.22288997798532859)
MB vs MB-HB Ttest_indResult(statistic=-1.857441603436155, pvalue=0.09020171862099477)
HB vs MB-HB Ttest_indResult(statistic=-0.2573893931263522, pvalue=0.8026664207572377)
----------------------------------------
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=6.687e-01 stat=-1.359e+00
HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-2.574e-01
MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=2.706e-01 stat=-1.857e+00
---------------- TSNE HB SC --------------------
HB vs SC Ttest_indResult(statistic=5.254255312815176, pvalue=4.519761202938194e-05)
HB vs HB-SC Ttest_indResult(statistic=1.883255242697052, pvalue=0.07295424528257502)
SC vs HB-SC Ttest_indResult(statistic=-2.0017571899759568, pvalue=0.05268355434431311)
----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 28.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=1.356e-04 stat=5.254e+00
SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=1.581e-01 stat=-2.002e+00
HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=2.189e-01 stat=1.883e+00
---------------- TSNE FB Pro. --------------------
FB vs Pro. Ttest_indResult(statistic=-3.232495710305294, pvalue=0.006018678276807629)
FB vs FB-Pro. Ttest_indResult(statistic=-7.680805951127082, pvalue=6.438564115580512e-08)
Pro. vs FB-Pro. Ttest_indResult(statistic=-3.0444027677687298, pvalue=0.006401768433269137)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.806e-02 stat=-3.232e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.921e-02 stat=-3.044e+00
FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.932e-07 stat=-7.681e+00
---------------- TSNE MB Pro. --------------------
MB vs Pro. Ttest_indResult(statistic=-1.0586367994017132, pvalue=0.3173497217983801)
MB vs MB-Pro. Ttest_indResult(statistic=-6.127176766542036, pvalue=2.6184650009305877e-05)
Pro. vs MB-Pro. Ttest_indResult(statistic=-4.2197532509456, pvalue=0.0007426847308432726)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=9.520e-01 stat=-1.059e+00
No handles with labels found to put in legend.
Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.228e-03 stat=-4.220e+00
MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=7.855e-05 stat=-6.127e+00
---------------- TSNE HB Pro. --------------------
HB vs Pro. Ttest_indResult(statistic=-0.21499973397073918, pvalue=0.8358971686045193)
HB vs HB-Pro. Ttest_indResult(statistic=-3.108271145114977, pvalue=0.011093287931107247)
Pro. vs HB-Pro. Ttest_indResult(statistic=-3.755246076157064, pvalue=0.0024033652036909987)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-2.150e-01
No handles with labels found to put in legend.
Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=7.210e-03 stat=-3.755e+00
HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=3.328e-02 stat=-3.108e+00
---------------- TSNE SC Pro. --------------------
SC vs Pro. Ttest_indResult(statistic=-4.227417154016156, pvalue=0.0003462156359161844)
SC vs SC-Pro. Ttest_indResult(statistic=-6.014935530825802, pvalue=4.5033333692920564e-07)
Pro. vs SC-Pro. Ttest_indResult(statistic=-2.1233623409289213, pvalue=0.04269550974099132)
----------------------------------------
p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.039e-03 stat=-4.227e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 55.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 54.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No handles with labels found to put in legend.
Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=1.281e-01 stat=-2.123e+00
SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=1.351e-06 stat=-6.015e+00

Save the genes as ordered lists for functional gene set tests.

We'll test each for functional enrichment

In [115]:
for label, data in data_m.items():
    for n in range(0, num_nodes):
        with open(f'{ora_dir}{label}-{n + 1}_{experiment_name}_{date}.csv', 'w+') as f:
            f.write(gene_id + ',value\n')
            desc_sorted = (data[:,n]).argsort()  # Sort the genes by descending order
            gene_names_sorted = df[gene_id].values[desc_sorted]
            vae_data_sorted = data[:,n][desc_sorted]
            for i, g in enumerate(gene_names_sorted):
                f.write(f'{g},{vae_data_sorted[i]}\n')
                
ora_dir
Out[115]:
'../../data/results/3_node_consistent_genes/functional/'

Save top 200 genes

In [120]:
n_genes = 200

def save_gene_order_lst(data, test_label):
    for n in range(0, num_nodes):
        with open(f'{ora_dir}{test_label}_{n}-max_cons-3_{date}.csv', 'w+') as f:
            f.write(gene_id + ',' + gene_name +  '\n')
            desc_sorted = (-data[:,n]).argsort()  # Sort the genes by descending order
            gene_names_sorted = df_training[gene_name].values[desc_sorted]
            gene_ids_sorted = df_training[gene_id].values[desc_sorted]
            for i in range(0, n_genes):
                f.write(f'{gene_ids_sorted[i]},{gene_names_sorted[i]}\n')

        with open(f'{ora_dir}{test_label}_{n}-min_cons-3_{date}.csv', 'w+') as f:
            f.write(gene_id + ',' + gene_name +  '\n')
            desc_sorted = (data[:,n]).argsort()  # Sort the genes by ascending order
            gene_names_sorted = df_training[gene_name].values[desc_sorted]
            gene_ids_sorted = df_training[gene_id].values[desc_sorted]
            for i in range(0, n_genes):
                f.write(f'{gene_ids_sorted[i]},{gene_names_sorted[i]}\n')

save_gene_order_lst(vae_data, 'VAE-deep')
save_gene_order_lst(vae_data_shallow, 'VAE-shallow')
save_gene_order_lst(pca_values, 'PCA')
save_gene_order_lst(umap_e, 'UMAP')
save_gene_order_lst(data_phate, 'PHATE')
save_gene_order_lst(data_tsne, 'TSNE')
In [104]:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from adjustText import adjust_text

from sciviso import Vis


class Volcanoplot(Vis):

    def __init__(self, df: pd.DataFrame, log_fc: str, p_val: str, label_column: str, title='',
                 xlabel='', ylabel='', invert=False, p_val_cutoff=0.05,
                 log_fc_cuttoff=2, label_big_sig=False, colours=None, offset=None,
                 text_colours=None, values_to_label=None, max_labels=10, values_colours=None,
                 figsize=(1.5, 1.5), title_font_size=8, label_font_size=6, title_font_weight=700):
        super().__init__(df, figsize=figsize, title_font_size=title_font_size, label_font_size=label_font_size,
                         title_font_weight=title_font_weight)
        super().__init__(df)
        self.log_fc = log_fc
        self.p_val = p_val
        self.p_val_cutoff = p_val_cutoff
        self.log_fc_cuttoff = log_fc_cuttoff
        self.values_to_label = values_to_label
        self.label_big_sig = label_big_sig
        self.invert = invert
        self.label_column = label_column
        self.offset = offset
        self.label = 'volcanoplot'
        self.colours = {'ns_small-neg-logFC': 'lightgrey',
                        'ns_small-pos-logFC': 'lightgrey',
                        'ns_big-neg-logFC': 'grey',
                        'ns_big-pos-logFC': 'grey',
                        'sig_small-neg-logFC': 'lightgrey',
                        'sig_small-pos-logFC': 'lightgrey',
                        'sig_big-neg-logFC': '#df80ff',
                        'sig_big-pos-logFC': '#ffa366'} if colours is None else colours
        self.xlabel = xlabel
        self.ylabel = ylabel
        self.title = title
        self.figsize = figsize
        self.max_labels = max_labels
        self.values_colours = values_colours or {}
        self.text_colours = text_colours or {}

    def add_scatter_and_annotate(self, fig: plt, x_all: np.array, y_all: np.array,
                                 colour: str, idxs: np.array, annotate=False, s=20):
        x = x_all[idxs]
        y = y_all[idxs]
        ax = fig.scatter(x, y, c=colour, alpha=self.opacity, s=s, vmin=-10.0, vmax=10.0)

        # Check if we want to annotate any of these with their gene IDs

        if self.values_to_label is not None:
            texts = []
            labels = self.df[self.label_column].values[idxs]
            for i, name in enumerate(labels):
                if name in self.values_to_label:
                    lbl_bg = self.values_colours.get(name)
                    color = self.text_colours.get(name)
                    texts.append(fig.text(x[i], y[i], name, color=color, fontsize=6,
                                          bbox=dict(fc="white", alpha=0.5, boxstyle='round,pad=0.1', lw=0)))
            adjust_text(texts, force_text=2.0)
        # Check if the user wants these labeled
        if self.label_big_sig and annotate:
            # If they do have a limit on the number of ones we show (i.e. we don't want 10000 gene names...)
            max_values = -1 * self.max_labels
            if len(y) < self.max_labels:
                max_values = -1 * (len(y) - 1)
            most_sig_idxs = np.argpartition(y, max_values)[max_values:]
            labels = self.df[self.label_column].values[idxs][most_sig_idxs]
            x = x[most_sig_idxs]
            y = y[most_sig_idxs]
            # We only label the ones with the max log fc
            for i, name in enumerate(labels):
                name = (' ').join(name.split('_')[1:]) #Format nicer
                fig.annotate(name, (x[i], y[i]),
                             xytext=(0, 10),
                             textcoords='offset points', ha='center', va='bottom',
                             bbox=dict(boxstyle='round,pad=0.25', 
                                       fc=colour, alpha=0.2)
                             )
        return ax

    def plot(self):
        """
        For annotation styling see: https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.annotate
        Returns
        -------

        """
        # if offset is not given, make the offset the smallest value in the dataset
        if not self.offset:
            vals = self.df[self.p_val].values
            self.offset = np.min(vals[np.nonzero(vals)])
            self.u.warn_p(['No offset was provided, setting offset to be smallest value recorded in dataset: ',
                           self.offset])

        # x axis has log_fc, first only plot the values < cutoff
        x = self.df[self.log_fc].values
        y = -1 * np.log10(self.df[self.p_val].values + self.offset)

        log_fc_np = self.df[self.log_fc].values
        p_val_np = self.df[self.p_val].values

        if self.invert:
            x = -1 * np.log10(self.df[self.p_val].values + self.offset)
            y = self.df[self.log_fc].values
        sig_small_pos_logfc = np.where((p_val_np <= self.p_val_cutoff) & (np.abs(log_fc_np) < self.log_fc_cuttoff)
                                       & (log_fc_np > 0))
        sig_big_pos_logfc = np.where((p_val_np <= self.p_val_cutoff) & (np.abs(log_fc_np) >= self.log_fc_cuttoff)
                                     & (log_fc_np > 0))

        sig_small_neg_logfc = np.where((p_val_np <= self.p_val_cutoff) & (np.abs(log_fc_np) < self.log_fc_cuttoff)
                                       & (log_fc_np <= 0))
        sig_big_neg_logfc = np.where((p_val_np <= self.p_val_cutoff) & (np.abs(log_fc_np) >= self.log_fc_cuttoff)
                                     & (log_fc_np <= 0))

        # Plot the points
        fig, ax = plt.subplots(figsize=self.figsize)
        self.add_scatter_and_annotate(ax, x, y, self.colours['sig_small-pos-logFC'], sig_small_pos_logfc)
        self.add_scatter_and_annotate(ax, x, y, self.colours['sig_big-pos-logFC'], 
                                      sig_big_pos_logfc, annotate=True, s=40)

        # Negative
        self.add_scatter_and_annotate(ax, x, y, self.colours['sig_small-neg-logFC'], sig_small_neg_logfc)
        self.add_scatter_and_annotate(ax, x, y, self.colours['sig_big-neg-logFC'], sig_big_neg_logfc, annotate=True, s=40)
        self.add_labels()
        ax.tick_params(labelsize=6)
        ax.tick_params(direction='out', length=2, width=0.5)
        ax.spines['bottom'].set_linewidth(0.5)
        ax.spines['top'].set_linewidth(0)
        ax.spines['left'].set_linewidth(0.5)
        ax.spines['right'].set_linewidth(0)
        ax.tick_params(labelsize=6)
        ax.tick_params(axis='x', which='major', pad=0)
        ax.tick_params(axis='y', which='major', pad=0)
        return ax
In [121]:
gsea_dir = '../../figures/3_node_consistent_genes/gsea/'
for f in os.listdir(gsea_dir):
    if 'GSEA' in f and 'KEGG' in f and 'Cluster' not in f:
        gsea_out = pd.read_csv(gsea_dir + f)
        print(f)
        volcanoplot = Volcanoplot(gsea_out, 'NES', 'padj', 'pathway', 
                                      f.split("_")[2], 'NES', '-log10(p adj)', 
                                      p_val_cutoff=1.0, max_labels=5,
                                      label_big_sig=True, log_fc_cuttoff=1.5, figsize=(2.0,2.0))
        sns.set_style("ticks")
        volcanoplot.plot()
        save_fig(f'Volcano_gsea_{f.split("_")[2]}')
        plt.show()
GSEA-unadj_UMAP_UMAP2_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.15097727920349488	
--------------------------------------------------------------------------------
GSEA-unadj_VAE-lin_VAE-lin3_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.0073029814917566	
--------------------------------------------------------------------------------
GSEA-unadj_PHATE_PHATE2_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.0013574947549179442	
--------------------------------------------------------------------------------
GSEA-unadj_PCA_PCA1_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.0012275564439229298	
--------------------------------------------------------------------------------
GSEA-unadj_VAE-lin_VAE-lin2_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.003342674193723028	
--------------------------------------------------------------------------------
GSEA-unadj_TSNE_TSNE1_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.00030417610280376336	
--------------------------------------------------------------------------------
GSEA-unadj_UMAP_UMAP3_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.14212056037060242	
--------------------------------------------------------------------------------
GSEA-unadj_PHATE_PHATE3_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.03309775177112748	
--------------------------------------------------------------------------------
GSEA-unadj_VAE-lin_VAE-lin1_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.00023870955294919864	
--------------------------------------------------------------------------------
GSEA-unadj_TSNE_TSNE2_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.009544807712405259	
--------------------------------------------------------------------------------
GSEA-unadj_PCA_PCA3_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.0061766083071522785	
--------------------------------------------------------------------------------
GSEA-unadj_TSNE_TSNE3_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.008438354355644731	
--------------------------------------------------------------------------------
GSEA-unadj_UMAP_UMAP1_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.001873590815145135	
--------------------------------------------------------------------------------
GSEA-unadj_PCA_PCA2_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.0046963770076152685	
--------------------------------------------------------------------------------
GSEA-unadj_PHATE_PHATE1_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.002077456888684494	
--------------------------------------------------------------------------------
In [106]:
gsea_dir = '../../figures/3_node_consistent_genes/gsea/'
for f in os.listdir(gsea_dir):
    if 'GSEA' in f and 'KEGG' in f:
        gsea_out = pd.read_csv(gsea_dir + f)
        print(f)
        volcanoplot = Volcanoplot(gsea_out, 'NES', 'padj', 'pathway', 
                                      f.split("_")[1], 'NES', '-log10(p adj)', 
                                      p_val_cutoff=1.0, max_labels=5,
                                      label_big_sig=True, log_fc_cuttoff=1.5, figsize=(2.0,2.0))
        sns.set_style("ticks")
        volcanoplot.plot()
        save_fig(f'Volcano_gsea_{f.split("_")[1]}_{f.split("_")[2]}')
        plt.show()
GSEA-unadj_Cluster2_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.01029582635077173	
--------------------------------------------------------------------------------
GSEA-unadj_Cluster3_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.0022298143204143197	
--------------------------------------------------------------------------------
GSEA-unadj_Cluster1_KEGG_.csv
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	0.01027047415281673	
--------------------------------------------------------------------------------
In [109]:
def save_fig(title, ending='.svg'):
    plt.savefig(f'{fig_dir}{title.replace(" ", "-")}{ending}')
    
for f in os.listdir(gsea_dir):
    if 'Go' in f and 'svg' not in f:
        gsea_out = pd.read_csv(gsea_dir + f)
        gsea_out = gsea_out.fillna(1.0)
        volcanoplot = Volcanoplot(gsea_out, 'NES', 'padj', 'pathway', 
                                      f.split("_")[1], 'NES', '-log10(p adj)', 
                                      p_val_cutoff=1.0, max_labels=5,
                                      label_big_sig=True, log_fc_cuttoff=1.5, figsize=(2,2))
        sns.set_style("ticks")
        volcanoplot.plot()
        save_fig(f'Volcano_gsea_GO_{f.split("_")[1]}_{f.split("_")[2]}', ending='.pdf' )
        plt.show()
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	5.241935483870968e-09	
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	9.225501255587968e-07	
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
No offset was provided, setting offset to be smallest value recorded in dataset: 	1.4130434782608697e-08	
--------------------------------------------------------------------------------